Methods and systems for the industrial internet of things

ABSTRACT

The methods and systems for data collection, processing, and utilization of signals with a platform monitoring at least a first element in a first machine in an industrial environment generally include obtaining, automatically with a computing environment, at least a first sensor signal and a second sensor signal with a local data collection system that monitors at least the first machine and connecting a first input of a crosspoint switch of the local data collection system to a first sensor and a second input of the crosspoint switch to a second sensor in the local data collection system. The methods and systems also include switching between a condition in which a first output of the crosspoint switch alternates between delivery of at least the first sensor signal and the second sensor signal and a condition in which there is simultaneous delivery of the first sensor signal from the first output and the second sensor signal from a second output of the crosspoint switch and switching off unassigned outputs of the crosspoint switch into a high-impedance state. The local data collection system includes multiple data acquisition units each having an onboard card set that store calibration information and maintenance history of a data acquisition unit in which the onboard card set is located.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a bypass continuation of International Pat. App. No.PCT/US17/31721, filed on 9 May 2017 and published on 16 Nov. 2017 asWO/2017/196821, which claims priority to U.S. Provisional Pat. App. No.62/333,589, filed 9 May 2016, entitled Strong Force Industrial IoTMatrix; U.S. Provisional Pat. App. No. 62/350,672, filed 15 Jun. 2016,entitled Strategy for High Sampling Rate Digital Recording ofMeasurement Waveform Data as Part of an Automated Sequential List thatStreams Long-Duration and Gap-Free Waveform Data to Storage for moreflexible Post-Processing; U.S. Provisional Pat. App. No. 62/412,843,filed 26 Oct. 2016, entitled Methods and Systems for the IndustrialInternet of Things; and U.S. Provisional Pat. App. No. 62/427,141, filed28 Nov. 2016, entitled Methods and Systems for the Industrial Internetof Things. All of the above applications are hereby incorporated byreference as if fully set forth herein.

BACKGROUND 1. Field

The present disclosure relates to methods and systems for datacollection in industrial environments, as well as methods and systemsfor leveraging collected data for monitoring, remote control, autonomousaction, and other activities in industrial environments.

2. Description of the Related Art

Heavy industrial environments, such as environments for large scalemanufacturing (such as of aircraft, ships, trucks, automobiles, andlarge industrial machines), energy production environments (such as oiland gas plants, renewable energy environments, and others), energyextraction environments (such as mining, drilling, and the like),construction environments (such as for construction of large buildings),and others, involve highly complex machines, devices and systems andhighly complex workflows, in which operators must account for a host ofparameters, metrics, and the like in order to optimize design,development, deployment, and operation of different technologies inorder to improve overall results. Historically, data has been collectedin heavy industrial environments by human beings using dedicated datacollectors, often recording batches of specific sensor data on media,such as tape or a hard drive, for later analysis. Batches of data havehistorically been returned to a central office for analysis, such as byundertaking signal processing or other analysis on the data collected byvarious sensors, after which analysis can be used as a basis fordiagnosing problems in an environment and/or suggesting ways to improveoperations. This work has historically taken place on a time scale ofweeks or months, and has been directed to limited data sets.

The emergence of the Internet of Things (IoT) has made it possible toconnect continuously to and among a much wider range of devices. Mostsuch devices are consumer devices, such as lights, thermostats, and thelike. More complex industrial environments remain more difficult, as therange of available data is often limited, and the complexity of dealingwith data from multiple sensors makes it much more difficult to produce“smart” solutions that are effective for the industrial sector. A needexists for improved methods and systems for data collection inindustrial environments, as well as for improved methods and systems forusing collected data to provide improved monitoring, control, andintelligent diagnosis of problems and intelligent optimization ofoperations in various heavy industrial environments.

SUMMARY

Methods and systems are provided herein for data collection inindustrial environments, as well as for improved methods and systems forusing collected data to provide improved monitoring, control, andintelligent diagnosis of problems and intelligent optimization ofoperations in various heavy industrial environments. These methods andsystems include methods, systems, components, devices, workflows,services, processes, and the like that are deployed in variousconfigurations and locations, such as: (a) at the “edge” of the Internetof Things, such as in the local environment of a heavy industrialmachine; (b) in data transport networks that move data between localenvironments of heavy industrial machines and other environments, suchas of other machines or of remote controllers, such as enterprises thatown or operate the machines or the facilities in which the machines areoperated; and (c) in locations where facilities are deployed to controlmachines or their environments, such as cloud-computing environments andon-premises computing environments of enterprises that own or controlheavy industrial environments or the machines, devices or systemsdeployed in them. These methods and systems include a range of ways forproviding improved data include a range of methods and systems forproviding improved data collection, as well as methods and systems fordeploying increased intelligence at the edge, in the network, and in thecloud or premises of the controller of an industrial environment.

Methods and systems are disclosed herein for continuous ultrasonicmonitoring, including providing continuous ultrasonic monitoring ofrotating elements and bearings of an energy production facility.

Methods and systems are disclosed herein for cloud-based, machinepattern recognition based on fusion of remote, analog industrialsensors.

Methods and systems are disclosed herein for cloud-based, machinepattern analysis of state information from multiple analog industrialsensors to provide anticipated state information for an industrialsystem.

Methods and systems are disclosed herein for on-device sensor fusion anddata storage for industrial IoT devices, including on-device sensorfusion and data storage for an Industrial IoT device, where data frommultiple sensors is multiplexed at the device for storage of a fuseddata stream.

Methods and systems are disclosed herein for a self-organizing datamarketplace for industrial IoT data, including a self-organizing datamarketplace for industrial IoT data, where available data elements areorganized in the marketplace for consumption by consumers based ontraining a self-organizing facility with a training set and feedbackfrom measures of marketplace success.

Methods and systems are disclosed herein for self-organizing data pools,including self-organization of data pools based on utilization and/oryield metrics, including utilization and/or yield metrics that aretracked for a plurality of data pools.

Methods and systems are disclosed herein for training artificialintelligence (“AI”) models based on industry-specific feedback,including training an AI model based on industry-specific feedback thatreflects a measure of utilization, yield, or impact, where the AI modeloperates on sensor data from an industrial environment.

Methods and systems are disclosed herein for a self-organized swarm ofindustrial data collectors, including a self-organizing swarm ofindustrial data collectors that organize among themselves to optimizedata collection based on the capabilities and conditions of the membersof the swarm.

Methods and systems are disclosed herein for an industrial IoTdistributed ledger, including a distributed ledger supporting thetracking of transactions executed in an automated data marketplace forindustrial IoT data.

Methods and systems are disclosed herein for a self-organizingcollector, including a self-organizing, multi-sensor data collector thatcan optimize data collection, power and/or yield based on conditions inits environment.

Methods and systems are disclosed herein for a network-sensitivecollector, including a network condition-sensitive, self-organizing,multi-sensor data collector that can optimize based on bandwidth,quality of service, pricing and/or other network conditions.

Methods and systems are disclosed herein for a remotely organizeduniversal data collector that can power up and down sensor interfacesbased on need and/or conditions identified in an industrial datacollection environment.

Methods and systems are disclosed herein for a self-organizing storagefor a multi-sensor data collector, including self-organizing storage fora multi-sensor data collector for industrial sensor data.

Methods and systems are disclosed herein for a self-organizing networkcoding for a multi-sensor data network, including self-organizingnetwork coding for a data network that transports data from multiplesensors in an industrial data collection environment.

Methods and systems are disclosed herein for a haptic or multi-sensoryuser interface, including a wearable haptic or multi-sensory userinterface for an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs.

Methods and systems are disclosed herein for a presentation layer foraugmented reality and virtual reality (AR/VR) industrial glasses, whereheat map elements are presented based on patterns and/or parameters incollected data.

Methods and systems are disclosed herein for condition-sensitive,self-organized tuning of AR/VR interfaces based on feedback metricsand/or training in industrial environments.

In embodiments, a system for data collection, processing, andutilization of signals from at least a first element in a first machinein an industrial environment includes a platform including a computingenvironment connected to a local data collection system having at leasta first sensor signal and a second sensor signal obtained from at leastthe first machine in the industrial environment. The system includes afirst sensor in the local data collection system configured to beconnected to the first machine and a second sensor in the local datacollection system. The system further includes a crosspoint switch inthe local data collection system having multiple inputs and multipleoutputs including a first input connected to the first sensor and asecond input connected to the second sensor. The multiple outputsinclude a first output and second output configured to be switchablebetween a condition in which the first output is configured to switchbetween delivery of the first sensor signal and the second sensor signaland a condition in which there is simultaneous delivery of the firstsensor signal from the first output and the second sensor signal fromthe second output. Each of multiple inputs is configured to beindividually assigned to any of the multiple outputs. Unassigned outputsare configured to be switched off producing a high-impedance state.

In embodiments, the first sensor signal and the second sensor signal arecontinuous vibration data about the industrial environment. Inembodiments, the second sensor in the local data collection system isconfigured to be connected to the first machine. In embodiments, thesecond sensor in the local data collection system is configured to beconnected to a second machine in the industrial environment. Inembodiments, the computing environment of the platform is configured tocompare relative phases of the first and second sensor signals. Inembodiments, the first sensor is a single-axis sensor and the secondsensor is a three-axis sensor. In embodiments, at least one of themultiple inputs of the crosspoint switch includes internet protocol,front-end signal conditioning, for improved signal-to-noise ratio. Inembodiments, the crosspoint switch includes a third input that isconfigured with a continuously monitored alarm having a pre-determinedtrigger condition when the third input is unassigned to any of themultiple outputs.

In embodiments, the local data collection system includes multiplemultiplexing units and multiple data acquisition units receivingmultiple data streams from multiple machines in the industrialenvironment. In embodiments, the local data collection system includesdistributed complex programmable hardware device (“CPLD”) chips eachdedicated to a data bus for logic control of the multiple multiplexingunits and the multiple data acquisition units that receive the multipledata streams from the multiple machines in the industrial environment.In embodiments, the local data collection system is configured toprovide high-amperage input capability using solid state relays. Inembodiments, the local data collection system is configured topower-down at least one of an analog sensor channel and a componentboard.

In embodiments, the local data collection system includes an externalvoltage reference for an A/D zero reference that is independent of thevoltage of the first sensor and the second sensor. In embodiments, thedistributed CPLD chips each dedicated to the data bus for logic controlof the multiple multiplexing units and the multiple data acquisitionunits includes as high-frequency crystal clock reference configured tobe divided by at least one of the distributed CPLD chips for at leastone delta-sigma analog-to-digital converter to achieve lower samplingrates without digital resampling.

In embodiments, the local data collection system is configured to obtainlong blocks of data at a single relatively high-sampling rate as opposedto multiple sets of data taken at different sampling rates. Inembodiments, the single relatively high-sampling rate corresponds to amaximum frequency of about forty kilohertz. In embodiments, the longblocks of data are for a duration that is in excess of one minute. Inembodiments, the local data collection system includes multiple dataacquisition units each having an onboard card set configured to storecalibration information and maintenance history of a data acquisitionunit in which the onboard card set is located. In embodiments, the localdata collection system is configured to plan data acquisition routesbased on hierarchical templates.

In embodiments, the local data collection system is configured to managedata collection bands. In embodiments, the data collection bands definea specific frequency band and at least one of a group of spectral peaks,a true-peak level, a crest factor derived from a time waveform, and anoverall waveform derived from a vibration envelope. In embodiments, thelocal data collection system includes a neural net expert system usingintelligent management of the data collection bands. In embodiments, thelocal data collection system is configured to create data acquisitionroutes based on hierarchical templates that each include the datacollection bands related to machines associated with the dataacquisition routes. In embodiments, at least one of the hierarchicaltemplates is associated with multiple interconnected elements of thefirst machine. In embodiments, at least one of the hierarchicaltemplates is associated with similar elements associated with at leastthe first machine and a second machine. In embodiments, at least one ofthe hierarchical templates is associated with at least the first machinebeing proximate in location to a second machine.

In embodiments, the local data collection system includes a graphicaluser interface (“GUI”) system configured to manage the data collectionbands. In embodiments, the GUI system includes an expert systemdiagnostic tool. In embodiments, the platform includes cloud-based,machine pattern analysis of state information from multiple sensors toprovide anticipated state information for the industrial environment. Inembodiments, the platform is configured to provide self-organization ofdata pools based on at least one of the utilization metrics and yieldmetrics. In embodiments, the platform includes a self-organized swarm ofindustrial data collectors. In embodiments, the local data collectionsystem includes a wearable haptic user interface for an industrialsensor data collector with at least one of vibration, heat, electrical,and sound outputs.

In embodiments, multiple inputs of the crosspoint switch include a thirdinput connected to the second sensor and a fourth input connected to thesecond sensor. The first sensor signal is from a single-axis sensor atan unchanging location associated with the first machine. Inembodiments, the second sensor is a three-axis sensor. In embodiments,the local data collection system is configured to record gap-freedigital waveform data simultaneously from at least the first input, thesecond input, the third input, and the fourth input. In embodiments, theplatform is configured to determine a change in relative phase based onthe simultaneously recorded gap-free digital waveform data. Inembodiments, the second sensor is configured to be movable to aplurality of positions associated with the first machine while obtainingthe simultaneously recorded gap-free digital waveform data. Inembodiments, multiple outputs of the crosspoint switch include a thirdoutput and fourth output. The second, third, and fourth outputs areassigned together to a sequence of tri-axial sensors each located atdifferent positions associated with the machine. In embodiments, theplatform is configured to determine an operating deflection shape basedon the change in relative phase and the simultaneously recorded gap-freedigital waveform data.

In embodiments, the unchanging location is a position associated withthe rotating shaft of the first machine. In embodiments, tri-axialsensors in the sequence of the tri-axial sensors are each located atdifferent positions on the first machine but are each associated withdifferent bearings in the machine. In embodiments, tri-axial sensors inthe sequence of the tri-axial sensors are each located at similarpositions associated with similar bearings but are each associated withdifferent machines. In embodiments, the local data collection system isconfigured to obtain the simultaneously recorded gap-free digitalwaveform data from the first machine while the first machine and asecond machine are both in operation. In embodiments, the local datacollection system is configured to characterize a contribution from thefirst machine and the second machine in the simultaneously recordedgap-free digital waveform data from the first machine. In embodiments,the simultaneously recorded gap-free digital waveform data has aduration that is in excess of one minute.

In embodiments, a method of monitoring a machine having at least oneshaft supported by a set of bearings includes monitoring a first datachannel assigned to a single-axis sensor at an unchanging locationassociated with the machine. The method includes monitoring second,third, and fourth data channels each assigned to an axis of a three-axissensor. The method includes recording gap-free digital waveform datasimultaneously from all of the data channels while the machine is inoperation and determining a change in relative phase based on thedigital waveform data.

In embodiments, the tri-axial sensor is located at a plurality ofpositions associated with the machine while obtaining the digitalwaveform. In embodiments, the second, third, and fourth channels areassigned together to a sequence of tri-axial sensors each located atdifferent positions associated with the machine. In embodiments, thedata is received from all of the sensors simultaneously. In embodiments,the method includes determining an operating deflection shape based onthe change in relative phase information and the waveform data. Inembodiments, the unchanging location is a position associated with theshaft of the machine. In embodiments, the tri-axial sensors in thesequence of the tri-axial sensors are each located at differentpositions and are each associated with different bearings in themachine. In embodiments, the unchanging location is a positionassociated with the shaft of the machine. The tri-axial sensors in thesequence of the tri-axial sensors are each located at differentpositions and are each associated with different bearings that supportthe shaft in the machine.

In embodiments, the method includes monitoring the first data channelassigned to the single-axis sensor at an unchanging location located ona second machine. The method includes monitoring the second, the third,and the fourth data channels, each assigned to the axis of a three-axissensor that is located at the position associated with the secondmachine. The method also includes recording gap-free digital waveformdata simultaneously from all of the data channels from the secondmachine while both of the machines are in operation. In embodiments, themethod includes characterizing the contribution from each of themachines in the gap-free digital waveform data simultaneously from thesecond machine.

In embodiments, a method for data collection, processing, andutilization of signals with a platform monitoring at least a firstelement in a first machine in an industrial environment includesobtaining, automatically with a computing environment, at least a firstsensor signal and a second sensor signal with a local data collectionsystem that monitors at least the first machine. The method includesconnecting a first input of a crosspoint switch of the local datacollection system to a first sensor and a second input of the crosspointswitch to a second sensor in the local data collection system. Themethod includes switching between a condition in which a first output ofthe crosspoint switch alternates between delivery of at least the firstsensor signal and the second sensor signal and a condition in whichthere is simultaneous delivery of the first sensor signal from the firstoutput and the second sensor signal from a second output of thecrosspoint switch. The method also includes switching off unassignedoutputs of the crosspoint switch into a high-impedance state.

In embodiments, the first sensor signal and the second sensor signal arecontinuous vibration data from the industrial environment. Inembodiments, the second sensor in the local data collection system isconnected to the first machine. In embodiments, the second sensor in thelocal data collection system is connected to a second machine in theindustrial environment. In embodiments, the method includes comparing,automatically with the computing environment, relative phases of thefirst and second sensor signals. In embodiments, the first sensor is asingle-axis sensor and the second sensor is a three-axis sensor. Inembodiments, at least the first input of the crosspoint switch includesinternet protocol front-end signal conditioning for improvedsignal-to-noise ratio.

In embodiments, the method includes continuously monitoring at least athird input of the crosspoint switch with an alarm having apre-determined trigger condition when the third input is unassigned toany of multiple outputs on the crosspoint switch. In embodiments, thelocal data collection system includes multiple multiplexing units andmultiple data acquisition units receiving multiple data streams frommultiple machines in the industrial environment. In embodiments, thelocal data collection system includes distributed (CPLD) chips eachdedicated to a data bus for logic control of the multiple multiplexingunits and the multiple data acquisition units that receive the multipledata streams from the multiple machines in the industrial environment.In embodiments, the local data collection system provides high-amperageinput capability using solid state relays.

In embodiments, the method includes powering down at least one of ananalog sensor channel and a component board of the local data collectionsystem. In embodiments, the local data collection system includes aphase-lock loop band-pass tracking filter that obtain slow-speed RPMsand phase information. In embodiments, the method includes digitallyderiving phase using on-board timers relative to at least one triggerchannel and at least one of multiple inputs on the crosspoint switch.

In embodiments, the method includes auto-scaling with a peak-detectorusing a separate analog-to-digital converter for peak detection. Inembodiments, the method includes routing at least one trigger channelthat is one of raw and buffered into at least one of multiple inputs onthe crosspoint switch. In embodiments, the method includes increasinginput oversampling rates with at least one delta-sigma analog-to-digitalconverter to reduce sampling rate outputs and to minimize anti-aliasingfilter requirements. In embodiments, the distributed CPLD chips are eachdedicated to the data bus for logic control of the multiple multiplexingunits and the multiple data acquisition units and each include ahigh-frequency crystal clock reference divided by at least one of thedistributed CPLD chips for at least one delta-sigma analog-to-digitalconverter to achieve lower sampling rates without digital resampling. Inembodiments, the method includes obtaining long blocks of data at asingle relatively high-sampling rate with the local data collectionsystem as opposed to multiple sets of data taken at different samplingrates. In embodiments, the single relatively high-sampling ratecorresponds to a maximum frequency of about forty kilohertz. Inembodiments, the long blocks of data are for a duration that is inexcess of one minute. In embodiments, the local data collection systemincludes multiple data acquisition units and each data acquisition unithas an onboard card set that stores calibration information andmaintenance history of a data acquisition unit in which the onboard cardset is located.

In embodiments, the method includes planning data acquisition routesbased on hierarchical templates associated with at least the firstelement in the first machine in the industrial environment. Inembodiments, the local data collection system manages data collectionbands that define a specific frequency band and at least one of a groupof spectral peaks, a true-peak level, a crest factor derived from a timewaveform, and an overall waveform derived from a vibration envelope. Inembodiments, the local data collection system includes a neural netexpert system using intelligent management of the data collection bands.In embodiments, the local data collection system creates dataacquisition routes based on hierarchical templates that each include thedata collection bands related to machines associated with the dataacquisition routes. In embodiments, at least one of the hierarchicaltemplates is associated with multiple interconnected elements of thefirst machine. In embodiments, at least one of the hierarchicaltemplates is associated with similar elements associated with at leastthe first machine and a second machine. In embodiments, at least one ofthe hierarchical templates is associated with at least the first machinebeing proximate in location to a second machine.

In embodiments, the method includes controlling a GUI system of thelocal data collection system to manage the data collection bands. TheGUI system includes an expert system diagnostic tool. In embodiments,the computing environment of the platform includes cloud-based, machinepattern analysis of state information from multiple sensors to provideanticipated state information for the industrial environment. Inembodiments, the computing environment of the platform providesself-organization of data pools based on at least one of the utilizationmetrics and yield metrics. In embodiments, the computing environment ofthe platform includes a self-organized swarm of industrial datacollectors. In embodiments, each of multiple inputs of the crosspointswitch is individually assignable to any of multiple outputs of thecrosspoint switch.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 through FIG. 5 are diagrammatic views that each depicts portionsof an overall view of an industrial Internet of Things (IoT) datacollection, monitoring and control system in accordance with the presentdisclosure.

FIG. 6 is a diagrammatic view of a platform including a local datacollection system disposed in an industrial environment for collectingdata from or about the elements of the environment, such as machines,components, systems, sub-systems, ambient conditions, states, workflows,processes, and other elements in accordance with the present disclosure.

FIG. 7 is a diagrammatic view that depicts elements of an industrialdata collection system for collecting analog sensor data in anindustrial environment in accordance with the present disclosure.

FIG. 8 is a diagrammatic view of a rotating or oscillating machinehaving a data acquisition module that is configured to collect waveformdata in accordance with the present disclosure.

FIG. 9 is a diagrammatic view of an exemplary tri-axial sensor mountedto a motor bearing of an exemplary rotating machine in accordance withthe present disclosure.

FIG. 10 and FIG. 11 are diagrammatic views of an exemplary tri-axialsensor and a single-axis sensor mounted to an exemplary rotating machinein accordance with the present disclosure.

FIG. 12 is a diagrammatic view of a multiple machines under survey withensembles of sensors in accordance with the present disclosure.

FIG. 13 is a diagrammatic view of hybrid relational metadata and abinary storage approach in accordance with the present disclosure.

FIG. 14 is a diagrammatic view of components and interactions of a datacollection architecture involving application of cognitive and machinelearning systems to data collection and processing in accordance withthe present disclosure.

FIG. 15 is a diagrammatic view of components and interactions of a datacollection architecture involving application of a platform having acognitive data marketplace in accordance with the present disclosure.

FIG. 16 is a diagrammatic view of components and interactions of a datacollection architecture involving application of a self-organizing swarmof data collectors in accordance with the present disclosure.

FIG. 17 is a diagrammatic view of components and interactions of a datacollection architecture involving application of a haptic user interfacein accordance with the present disclosure.

DETAILED DESCRIPTION

Detailed embodiments of the present disclosure are disclosed herein;however, it is to be understood that the disclosed embodiments aremerely exemplary of the disclosure, which may be embodied in variousforms. Therefore, specific structural and functional details disclosedherein are not to be interpreted as limiting, but merely as a basis forthe claims and as a representative basis for teaching one skilled in theart to variously employ the present disclosure in virtually anyappropriately detailed structure.

The terms “a” or “an,” as used herein, are defined as one or more thanone. The term “another,” as used herein, is defined as at least a secondor more. The terms “including” and/or “having,” as used herein, aredefined as comprising (i.e., open transition).

While only a few embodiments of the present disclosure have been shownand described, it will be obvious to those skilled in the art that manychanges and modifications may be made thereunto without departing fromthe spirit and scope of the present disclosure as described in thefollowing claims. All patent applications and patents, both foreign anddomestic, and all other publications referenced herein are incorporatedherein in their entireties to the full extent permitted by law.

FIGS. 1 through 5 depict portions of an overall view of an industrialInternet of Things (IoT) data collection, monitoring and control system10. FIG. 2 shows an upper left portion of a schematic view of anindustrial IoT system 10 of FIGS. 1-5. FIG. 2 includes a mobile ad hocnetwork (“MANET”) 20, which may form a secure, temporal networkconnection 22 (sometimes connected and sometimes isolated), with a cloud30 or other remote networking system, so that network functions mayoccur over the MANET 20 within the environment, without the need forexternal networks, but at other times information can be sent to andfrom a central location. This allows the industrial environment to usethe benefits of networking and control technologies, while alsoproviding security, such as preventing cyber-attacks. The MANET 20 mayuse cognitive radio technologies 40, including ones that form up anequivalent to the IP protocol, such as router 42, MAC 44, and physicallayer technologies 46. Also, depicted is network-sensitive ornetwork-aware transport of data over the network to and from a datacollection device or a heavy industrial machine.

FIG. 3 shows the upper right portion of a schematic view of anindustrial IoT system 10 of FIGS. 1 through 5. This includes intelligentdata collection technologies 50 deployed locally, at the edge of an IoTdeployment, where heavy industrial machines are located. This includesvarious sensors 52, IoT devices 54, data storage capabilities 56(including intelligent, self-organizing storage), sensor fusion(including self-organizing sensor fusion) and the like. FIG. 3 showsinterfaces for data collection, including multi-sensory interfaces,tablets, smartphones 58, and the like. FIG. 3 also shows data pools 60that may collect data published by machines or sensors that detectconditions of machines, such as for later consumption by local or remoteintelligence. A distributed ledger system 62 may distribute storageacross the local storage of various elements of the environment, or morebroadly throughout the system.

FIG. 1 shows a center portion of a schematic view of an industrial IoTsystem of FIGS. 1 through 5. This includes use of network coding(including self-organizing network coding) that configures a networkcoding model based on feedback measures, network conditions, or thelike, for highly efficient transport of large amounts of data across thenetwork to and from data collection systems and the cloud. In the cloudor on an enterprise owner's or operator's premises may be deployed awide range of capabilities for intelligence, analytics, remote control,remote operation, remote optimization, and the like, including a widerange of capabilities depicted in FIG. 1. This includes various storageconfigurations, which may include distributed ledger storage, such asfor supporting transactional data or other elements of the system.

FIGS. 1, 4, and 5 show the lower right corner of a schematic view of anindustrial IoT system of FIGS. 1 through 5. This includes a programmaticdata marketplace 70, which may be a self-organizing marketplace, such asfor making available data that is collected in industrial environments,such as from data collectors, data pools, distributed ledgers, and otherelements disclosed herein and depicted in FIGS. 1 through 5. FIGS. 1, 4,and 5 also show on-device sensor fusion 80, such as for storing on adevice data from multiple analog sensors 82, which may be analyzedlocally or in the cloud, such as by machine learning 84, including bytraining a machine based on initial models created by humans that areaugmented by providing feedback (such as based on measures of success)when operating the methods and systems disclosed herein. Additionaldetail on the various components and sub-components of FIGS. 1 through 5is provided throughout this disclosure.

In embodiments, methods and systems are provided for a system for datacollection, processing, and utilization in an industrial environment,referred to herein as the platform 100. With reference to FIG. 6, theplatform 100 may include a local data collection system 102, which maybe disposed in an environment 104, such as an industrial environment,for collecting data from or about the elements of the environment, suchas machines, components, systems, sub-systems, ambient conditions,states, workflows, processes, and other elements. The platform 100 mayconnect to or include portions of the industrial IoT data collection,monitoring and control system 10 depicted in FIGS. 1-5. The platform 100may include a network data transport system 108, such as fortransporting data to and from the local data collection system 102 overa network 110, such as to a host processing system 112, such as one thatis disposed in a cloud computing environment or on the premises of anenterprise, or that consists of distributed components that interactwith each other to process data collected by the local data collectionsystem 102. The host processing system 112, referred to for conveniencein some cases as the host system 112, may include various systems,components, methods, processes, facilities, and the like for enablingautomated, or automation-assisted processing of the data, such as formonitoring one or more environments 104 or networks 110 or for remotelycontrolling one or more elements in a local environment 104 or in anetwork 110. The platform 100 may include one or more local autonomoussystems 114, such as for enabling autonomous behavior, such asreflecting artificial, or machine-based intelligence or such as enablingautomated action based on the applications of a set of rules or modelsupon input data from the local data collection system 102 or from one ormore input sources 116, which may comprise information feeds and inputsfrom a wide array of sources, including ones in the local environment104, in a network 110, in the host system 112, or in one or moreexternal systems, databases, or the like. The platform 100 may includeone or more intelligent systems 118, which may be disposed in,integrated with, or acting as inputs to one or more components of theplatform 100. Details of these and other components of the platform 100are provided throughout this disclosure.

Intelligent systems may include cognitive systems 120, such as enablinga degree of cognitive behavior as a result of the coordination ofprocessing elements, such as mesh, peer-to-peer, ring, serial and otherarchitectures, where one or more node elements is coordinated with othernode elements to provide collective, coordinated behavior to assist inprocessing, communication, data collection, or the like. The MANET 20depicted in FIG. 2 may also use cognitive radio technologies, includingones that form up an equivalent to the IP protocol, such as router 42,MAC 44, and physical layer technologies 46. In one example, thecognitive system technology stack can include examples disclosed in U.S.Pat. No. 8,060,017 to Schlicht et al., issued 15 Nov. 2011 and herebyincorporated by reference as if fully set forth herein. Intelligentsystems may include machine learning systems 122, such as for learningon one or more data sets. The one or may data sets may includeinformation collections using local data collection systems 102 or otherinformation from input sources 116, such as to recognize states,objects, events, patterns, conditions, or the like that may in turn beused for processing by the processing system 112 as inputs to componentsof the platform 100 and portions of the industrial IoT data collection,monitoring and control system 10, or the like. Learning may behuman-supervised or fully-automated, such as using one or more inputsources 116 to provide a data set, along with information about the itemto be learned. Machine learning may use one or more models, rules,semantic understandings, workflows, or other structured orsemi-structured understanding of the world, such as for automatedoptimization of control of a system or process based on feedback or feedforward to an operating model for the system or process. One suchmachine learning technique for semantic and contextual understandings,workflows, or other structured or semi-structured understandings isdisclosed in U.S. Pat. No. 8,200,775 to Moore, issued 12 Jun. 2012 andhereby incorporated by reference as if fully set forth herein. Machinelearning may be used to improve the foregoing, such as by adjusting oneor more weights, structures, rules, or the like (such as changing afunction within a model) based on feedback (such as regarding thesuccess of a model in a given situation) or based on iteration (such asin a recursive process). Where sufficient understanding of theunderlying structure or behavior of a system is not known, insufficientdata is not available, or in other cases where preferred for variousreasons, machine learning may also be undertaken in the absence of anunderlying model; that is, input sources may be weighted, structured, orthe like within a machine learning facility without regard to any apriori understanding of structure, and outcomes (such as based onmeasures of success at accomplishing various desired objectives) can beserially fed to the machine learning system to allow it to learn how toachieve the targeted objectives. For example, the system may learn torecognize faults, to recognize patterns, to develop models or functions,to develop rules, to optimize performance, to minimize failure rates, tooptimize profits, to optimize resource utilization, to optimize flow(such as of traffic), or to optimize many other parameters that may berelevant to successful outcomes (such as in a wide range ofenvironments). Machine learning may use genetic programming techniques,such as promoting or demoting one or more input sources, structures,data types, objects, weights, nodes, links, or other factors based onfeedback (such that successful elements emerge over a series ofgenerations). For example, alternative available sensor inputs for adata collection system 102 may be arranged in alternative configurationsand permutations, such that the system may, using genetic programmingtechniques over a series of data collection events, determine whatpermutations provide successful outcomes based on various conditions(such as conditions of components of the platform 100, conditions of thenetwork 110, conditions of a data collection system 102, conditions ofan environment 104), or the like. In embodiments, local machine learningmay turn on or off one or more sensors in a multi-sensor data collector102 in permutations over time, while tracking success outcomes (such ascontributing to success in predicting a failure, contributing to aperformance indicator (such as efficiency, effectiveness, return oninvestment, yield, or the like), contributing to optimization of one ormore parameters, identification of a pattern (such as relating to athreat, a failure mode, a success mode, or the like) or the like. Forexample, a system may learn what sets of sensors should be turned on oroff under given conditions to achieve the highest value utilization of adata collector 102. In embodiments, similar techniques may be used tohandle optimization of transport of data in the platform 100 (such as inthe network 110, by using genetic programming or other machine learningtechniques to learn to configure network elements (such as configuringnetwork transport paths, configuring network coding types andarchitectures, configuring network security elements), and the like.

In embodiments, the local data collection system 102 may include ahigh-performance, multi-sensor data collector having a number of novelfeatures for collection and processing of analog and other sensor data.In embodiments, a local data collection system 102 may be deployed tothe industrial facilities depicted in FIG. 3. A local data collectionsystem 102 may also be deployed monitor other machines such as themachine 2300 in FIG. 9 and FIG. 10, the machines 2400, 2600, 2800, 2950,3000 depicted in FIG. 12, and the machines 3202, 3204 depicted in FIG.13. The data collection system 102 may have on board intelligent systems(such as for learning to optimize the configuration and operation of thedata collector, such as configuring permutations and combinations ofsensors based on contexts and conditions). In one example, the datacollection system 102 includes a crosspoint switch 130. Automated,intelligent configuration of the local data collection system 102 may bebased on a variety of types of information, such as from various inputsources, such as based on available power, power requirements ofsensors, the value of the data collected (such as based on feedbackinformation from other elements of the platform 100), the relative valueof information (such as based on the availability of other sources ofthe same or similar information), power availability (such as forpowering sensors), network conditions, ambient conditions, operatingstates, operating contexts, operating events, and many others.

FIG. 7 shows elements and sub-components of a data collection andanalysis system 1100 for sensor data (such as analog sensor data)collected in industrial environments. As depicted in FIG. 7, embodimentsof the methods and systems disclosed herein may include hardware thathas several different modules starting with the multiplexer (“Mux”)1104. In embodiments, the Mux 1104 is made up of a main board and anoption board 1108. The main board is where the sensors connect to thesystem. These connections are on top to enable ease of installation.Then there are numerous settings on the underside of this board as wellas on the Mux option board, which attaches to the main board via twoheaders one at either end of the board. In embodiments, the Mux optionboard has the male headers, which mesh together with the female headeron the main Mux board. This enables them to be stacked on top of eachother taking up less real estate.

In embodiments, the main Mux then connects to the mother (e.g., with 4simultaneous channels) and daughter (e.g., with 4 additional channelsfor 8 total channels) analog boards 1110 via cables where some of thesignal conditioning (such as hardware integration) occurs. The signalsthen move from the analog boards 1110 to the anti-aliasing board wheresome of the potential aliasing is removed. The rest of the aliasing isdone on the delta sigma board 1112, which it connects to through cables.The delta sigma board 1112 provides more aliasing protection along withother conditioning and digitizing of the signal. Next, the data moves tothe Jennic™ board 1114 for more digitizing as well as communication to acomputer via USB or Ethernet. In embodiments, the Jennic™ board 1114 maybe replaced with a pic board 1118 for more advanced and efficient datacollection as well as communication. Once the data moves to the computersoftware 1102, the computer software 1102 can manipulate the data toshow trending, spectra, waveform, statistics, and analytics.

In embodiments, the system is meant to take in all types of data fromvolts to 4-20 mA signals. In embodiments, open formats of data storageand communication may be used. In some instances, certain portions ofthe system may be proprietary especially some of research and dataassociated with the analytics and reporting. In embodiments, smart bandanalysis is a way to break data down into easily analyzed parts that canbe combined with other smart bands to make new more simplified yetsophisticated analytics. In embodiments, this unique information istaken and graphics are used to depict the conditions because picturedepictions are more helpful to the user. In embodiments, complicatedprograms and user interfaces are simplified so that any user canmanipulate the data like an expert.

In embodiments, the system in essence, works in a big loop. It starts insoftware with a general user interface. Most, if not all, online systemsrequire the OEM to create or develop the system GUI 1124. Inembodiments, rapid route creation takes advantage of hierarchicaltemplates. In embodiments, a “GUI” is created so any general user canpopulate the information itself with simple templates. Once thetemplates are created the user can copy and paste whatever the userneeds. In addition, users can develop their own templates for futureease of use and institutionalizing the knowledge. When the user hasentered all of the user's information and connected all of the user'ssensors, the user can then start the system acquiring data. In someapplications, rotating machinery can build up an electric charge whichcan harm electrical equipment. In embodiments, in order to diminish thischarge's effect on the equipment, a unique electrostatic protection fortrigger and vibration inputs is placed upfront on the Mux and DAQhardware in order to dissipate this electric charge as the signal passedfrom the sensor to the hardware. In embodiments, the Mux and analogboard also can offer upfront circuitry and wider traces in high-amperageinput capability using solid state relays and design topology thatenables the system to handle high amperage inputs if necessary.

In embodiments, an important part at the front of the Mux is up frontsignal conditioning on Mux for improved signal-to-noise ratio whichprovides upfront signal conditioning. Most multiplexers are afterthoughts and the original equipment manufacturers usually do not worryor even think about the quality of the signal coming from it. As aresult, the signals quality can drop as much as 30 dB or more. Everysystem is only as strong as its weakest link, so no matter if you have a24 bit DAQ that has a S/N ratio of 110 dB, your signal quality hasalready been lost through the Mux. If the signal to noise ratio hasdropped to 80 dB in the Mux, it may not be much better than a 16-bitsystem from 20 years ago.

In embodiments, in addition to providing a better signal, themultiplexer also can play a key role in enhancing a system. Trulycontinuous systems monitor every sensor all the time but these systemsare very expensive. Multiplexer systems can usually only monitor a setnumber of channels at one time and switches from bank to bank from alarger set of sensors. As a result, the sensors not being collected onare not being monitored so if a level increases the user may never know.In embodiments, a multiplexer continuous monitor alarming featureprovides a continuous monitoring alarming multiplexer by placingcircuitry on the multiplexer that can measure levels against knownalarms even when the data acquisition (“DAQ”) is not monitoring thechannel. This in essence makes the system continuous without the abilityto instantly capture data on the problem like a true continuous system.In embodiments, coupling this capability to alarm with adaptivescheduling techniques for continuous monitoring and the continuousmonitoring system's software adapting and adjusting the data collectionsequence based on statistics, analytics, data alarms and dynamicanalysis the system will be able to quickly collect dynamic spectraldata on the alarming sensor very soon after the alarm sounds.

Another restriction of multiplexers is that they often have a limitednumber of channels. In embodiments, use of distributed complexprogrammable logic device (“CPLD”) chips with dedicated bus for logiccontrol of multiple Mux and data acquisition sections enables a CPLD tocontrol multiple mux and DAQs so that there is no limit to the number ofchannels a system can handle. In embodiments, multiplexers and DAQs canstack together offering additional input and output channels to thesystem.

Besides having limited number of channels, multiplexers also usually canonly collect sensors in the same bank. For detailed analysis, this isvery limiting as there is tremendous value in being able to review datasimultaneously from sensors on the same machine. In embodiments, use ofan analog crosspoint switch for collecting variable groups of vibrationinput channels addresses this issue by using a crosspoint switch whichis often used in the phone industry and provides a matrix circuit so thesystem can access any set of eight channels from the total number ofinput sensors.

In embodiments, the system provides all the same capabilities as onsitewill allow phase-lock-loop band pass tracking filter method forobtaining slow-speed revolutions per minute (“RPM”) and phase forbalancing purposes to remotely balance slow speed machinery such as inpaper mills as well as offer additional analysis from its data.

In embodiments, ability to control multiple multiplexers with use ofdistributed CPLD chips with dedicated bus for logic control of multipleMux and data acquisition sections is enhanced with a hierarchicalmultiplexer which allows for multiple DAQ to collect data from multiplemultiplexers. In embodiments, this allows for faster data collection aswell as more channels of simultaneous data collection which enhancesanalysis. In embodiments, the Mux may be configured slightly to make itportable and use data acquisition parking features, which turns SV3X DAQinto a protect system.

In embodiments, once the signals leave the multiplexer and hierarchicalMux they move to the analog board where there are other enhancements. Inembodiments, power-down of analog channels when not in use as well otherpower-saving measures including powering down of component boards allowthe system to power down channels on the mother and the daughter analogboards in order to save power. In embodiments, this can offer the samepower saving benefits to a protect system especially if it is batteryoperated or solar powered. In embodiments, in order to maximize thesignal to noise ratio and provide the best data, a peak-detector forauto-scaling routed into a separate A/D will provide the system thehighest peak in each set of data so it can rapidly scale the data tothat peak. In embodiments, improved integration using both analog anddigital methods create an innovative hybrid integration which alsoimproves or maintains the highest possible signal to noise ratio.

In embodiments, a section of the analog board allows routing of atrigger channel, either raw or buffered, into other analog channels.This allows users to route the trigger to any of the channels foranalysis and trouble shooting. In embodiments, once the signals leavethe analog board, the signals move into the delta-sigma board whereprecise voltage reference for A/D zero reference offers more accuratedirect current sensor data. The delta sigma's high speeds also providefor using higher input oversampling for delta-sigma A/D for lowersampling rate outputs to minimize antialiasing filter requirements tooversample the data at a higher input which minimizes anti-aliasingrequirements. In embodiments, a CPLD may be used as a clock-divider fora delta-sigma A/D to achieve lower sampling rates without the need fordigital resampling so the delta-sigma A/D can achieve lower samplingrates without digitally resampling the data.

In embodiments, the data then moves from the delta-sigma board to theJennic™ board where digital derivation of phase relative to input andtrigger channels using on-board timers digitally derives the phase fromthe input signal and the trigger using on board timers. In embodiments,the Jennic™ board also has the ability to store calibration data andsystem maintenance repair history data in an on-board card set. Inembodiments, the Jennic™ board will enable acquiring long blocks of dataat high-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates so it can stream data and acquire long blocksof data for advanced analysis in the future.

In embodiments, after the signal moves through the Jennic™ board it isthen transmitted to the computer. Once on the computer, the software hasa number of enhancements that improve the systems analytic capabilities.In embodiments, rapid route creation takes advantage of hierarchicaltemplates and provides rapid route creation of all the equipment usingsimple templates which also speeds up the software deployment. Inembodiments, the software will be used to add intelligence to thesystem. It will start with an expert system GUIs graphical approach todefining smart bands and diagnoses for the expert system, which willoffer a graphical expert system with simplified user interface so anyonecan develop complex analytics. In embodiments, this user interface willrevolve around smart bands, which are a simplified approach to complexyet flexible analytics for the general user. In embodiments, the smartbands will pair with a self-learning neural network for an even moreadvanced analytical approach. In embodiments, this system will also usethe machine's hierarchy for additional analytical insight. One criticalpart of predictive maintenance is the ability to learn from knowninformation during repairs or inspections. In embodiments, graphicalapproaches for back calculations may improve the smart bands andcorrelations based on a known fault or problem.

In embodiments, besides detailed analysis via smart bands, a bearinganalysis method is provided. In recent years, there has been a strongdrive in industry to save power which has resulted in an influx ofvariable frequency drives. In embodiments, torsional vibration detectionand analysis utilizing transitory signal analysis provides an advancedtorsional vibration analysis for a more comprehensive way to diagnosemachinery where torsional forces are relevant (such as machinery withrotating components). In embodiments, the system can deploy a number ofintelligent capabilities on its own for better data and morecomprehensive analysis. In embodiments, this intelligence will startwith a smart route where the software's smart route can adapt thesensors it collects simultaneously in order to gain additionalcorrelative intelligence. In embodiments, smart operational data store(“ODS”) allows the system to elect to gather operational deflectionshape analysis in order to further examine the machinery condition. Inembodiments, besides changing the route, adaptive scheduling techniquesfor continuous monitoring allow the system to change the scheduled datacollected for full spectral analysis across a number (e.g., eight), ofcorrelative channels. The systems intelligence will provide data toenable extended statistics capabilities for continuous monitoring aswell as ambient local vibration for analysis that combines ambienttemperature and local temperature and vibration levels changes foridentifying machinery issues.

Embodiments of the methods and systems disclosed herein may include aself-sufficient DAQ box. In embodiments, a data acquisition device maybe controlled by a personal computer (“PC”) to implement the desireddata acquisition commands. In embodiments, the system has the ability tobe self-sufficient and can acquire, process, analyze and monitorindependent of external PC control. Embodiments of the methods andsystems disclosed herein may include secure digital (“SD”) card storage.In embodiments, significant additional storage capability is providedutilizing an SD card such as cameras, smart phones, and so on. This canprove critical for monitoring applications where critical data can bestored permanently. Also, if a power failure should occur, the mostrecent data may be stored despite the fact that it was not off-loaded toanother system. Embodiments of the methods and systems disclosed hereinmay include a DAQ system. A current trend has been to make DAQ systemsas communicative as possible with the outside world usually in the formof networks including wireless. Whereas in the past it was common to usea dedicated bus to control a DAQ system with either a microprocessor ormicrocontroller/microprocessor paired with a PC, today the demands fornetworking are much greater and so it is out of this environment thatarises this new design prototype. In embodiments, multiplemicroprocessor/microcontrollers or dedicated processors may be utilizedto carry out various aspects of this increase in DAQ functionality withone or more processor units focused primarily on the communicationaspects with the outside world. This negates the need for constantlyinterrupting the main processes which include the control of the signalconditioning circuits, triggering, raw data acquisition using the A/D,directing the A/D output to the appropriate on-board memory andprocessing that data. In embodiments, a specializedmicrocontroller/microprocessor is designated for all communications withthe outside. These include USB, Ethernet and wireless with the abilityto provide an IP address or addresses in order to host a webpage. Allcommunications with the outside world are then accomplished using asimple text based menu. The usual array of commands (in practice morethan a hundred) such as InitializeCard, AcquireData, StopAcquisition,RetrieveCalibration Info, and so on, would be provided. In addition, inembodiments, other intense signal processing activities includingresampling, weighting, filtering, and spectrum processing can beperformed by dedicated processors such as field-programmable gate array(“FPGAs”), digital signal processor (“DSP”), microprocessors,micro-controllers, or a combination thereof. In embodiments, thissubsystem will communicate via a specialized hardware bus with thecommunication processing section. It will be facilitated with dual-portmemory, semaphore logic, and so on. This embodiment will not onlyprovide a marked improvement in efficiency but can significantly improvethe processing capability, including the streaming of the data as wellother high-end analytical techniques.

Embodiments of the methods and systems disclosed herein may includesensor overload identification. A need exists for monitoring systems toidentify when the sensor is overloading. A monitoring system mayidentify when their system is overloading, but in embodiments, thesystem may look at the voltage of the sensor to determine if theoverload is from the sensor, which is useful to the user to get anothersensor better suited to the situation, or the user can try to gather thedata again. There are often situations involving high frequency inputsthat will saturate a standard 100 mv/g sensor (which is most commonlyused in the industry) and having the ability to sense the overloadimproves data quality for better analysis.

Embodiments of the methods and systems disclosed herein may includeradio frequency identification (“RF ID”) and inclinometer onaccelerometer or RF ID on other sensors so the sensor can tell thesystem/software what machine/bearing and direction it is attached to andcan automatically set it up in the software to store the data withoutthe user telling it. In embodiments, users could, in turn, put thesystem on any machine or machines and the system would automatically setitself up and be ready for data collection in seconds

Embodiments of the methods and systems disclosed herein may includeultrasonic online monitoring by placing ultrasonic sensors insidetransformers, motor control centers, breakers and the like where thesystem will monitor via a sound spectrum continuously looking forpatterns that identify arcing, corona and other electrical issuesindicating a break down or issue. In embodiments, an analysis enginewill be used in ultrasonic online monitoring as well as identifyingother faults by combining this data with other parameters such asvibration, temperature, pressure, heat flux, magnetic fields, electricalfields, currents, voltage, capacitance, inductance, and combinations(e.g., simple ratios) of the same, among many others.

Embodiments of the methods and systems disclosed herein may include useof an analog crosspoint switch for collecting variable groups ofvibration input channels. For vibration analysis, it is useful to obtainmultiple channels simultaneously from vibration transducers mounted ondifferent parts of a machine (or machines) in multiple directions. Byobtaining the readings at the same time, for example, the relativephases of the inputs may be compared for the purpose of diagnosingvarious mechanical faults. Other types of cross channel analyses such ascross-correlation, transfer functions, Operating Deflection Shape(“ODS”) may also be performed. Current systems using conventional fixedbank multiplexers can only compare a limited number of channels (basedon the number of channels per bank) that were assigned to a particulargroup at the time of installation. The only way to provide someflexibility is to either overlap channels or incorporate lots ofredundancy in the system both of which can add considerable expense (insome cases an exponential increase in cost versus flexibility). Thesimplest Mux design selects one of many inputs and routes it into asingle output line. A banked design would consist of a group of thesesimple building blocks, each handling a fixed group of inputs androuting to its respective output. Typically, the inputs are notoverlapping so that the input of one Mux grouping cannot be routed intoanother. Unlike conventional Mux chips which typically switch a fixedgroup or banks of a fixed selection of channels into a single output(e.g., in groups of 2, 4, 8, etc.), a crosspoint Mux allows the user toassign any input to any output. Previously, crosspoint multiplexers wereused for specialized purposes such as RGB digital video applications andwere as a practical matter too noisy for analog applications such asvibration analysis; however more recent advances in the technology nowmake it feasible. Another advantage of the crosspoint Mux is the abilityto disable outputs by putting them into a high impedance state. This isideal for an output bus so that multiple Mux cards may be stacked andtheir output buses joined together without the need for bus switches.

Embodiments of the methods and systems disclosed herein may include upfront signal conditioning on Mux for improved signal-to-noise ratio.Embodiments may perform signal conditioning (such as range/gain control,integration, filtering, etc.) on vibration as well as other signalinputs up front before Mux switching to achieve the highestsignal-to-noise ratio.

Embodiments of the methods and systems disclosed herein may include aMux continuous monitor alarming feature. In embodiments, continuousmonitoring Mux bypass offers a mechanism whereby channels not beingcurrently sampled by the Mux system may be continuously monitored forsignificant alarm conditions via a number of trigger conditions usingfiltered peak-hold circuits or functionally similar that are in turnpassed on to the monitoring system in an expedient manner using hardwareinterrupts or other means.

Embodiments of the methods and systems disclosed herein may include useof distributed CPLD chips with dedicated bus for logic control ofmultiple Mux and data acquisition sections. Interfacing to multipletypes of predictive maintenance and vibration transducers requires agreat deal of switching. This includes AC/DC coupling, 4-20 interfacing,integrated electronic piezoelectric transducer, channel power-down (forconserving op amp power), single-ended or differential groundingoptions, and so on. Also required is the control of digital pots forrange and gain control, switches for hardware integration, AA filteringand triggering. This logic can be performed by a series of CPLD chipsstrategically located for the tasks they control. A single giant CPLDrequires long circuit routes with a great deal of density at the singlegiant CPLD. In embodiments, distributed CPLDs not only address theseconcerns but offer a great deal of flexibility. A bus is created whereeach CPLD that has a fixed assignment has its own unique device address.For multiple boards (e.g., for multiple Mux boards), jumpers areprovided for setting multiple addresses. In another example, three bitscan permit up to 8 boards that are jumper configurable. In embodiments,a bus protocol is defined such that each CPLD on the bus can either beaddressed individually or as a group.

Embodiments of the methods and systems disclosed herein may includehigh-amperage input capability using solid state relays and designtopology. Typically, vibration data collectors are not designed tohandle large input voltages due to the expense and the fact that, moreoften than not, it is not needed. A need exists for these datacollectors to acquire many varied types of PM data as technologyimproves and monitoring costs plummet. In embodiments, a method is usingthe already established OptoMOS™ technology which can permit theswitching up front of high voltage signals rather than using moreconventional reed-relay approaches. Many historic concerns regardingnon-linear zero crossing or other non-linear solid-state behaviors havebeen eliminated with regard to the passing through of weakly bufferedanalog signals. In addition, in embodiments, printed circuit boardrouting topologies place all of the individual channel input circuitryas close to the input connector as possible.

Embodiments of the methods and systems disclosed herein may includepower-down of analog channels when not in use as well other power-savingmeasures including powering down of component boards. In embodiments,power-down of analog signal processing op-amps for non-selected channelsas well as the ability to power down component boards and other hardwareby the low-level firmware for the DAQ system makes high-levelapplication control with respect to power-saving capabilities relativelyeasy. Explicit control of the hardware is always possible but notrequired by default.

Embodiments of the methods and systems disclosed herein may includeunique electrostatic protection for trigger and vibration inputs. Inmany critical industrial environments where large electrostatic forcesmay build up, for example low-speed balancing using large belts, propertransducer and trigger input protection is required. In embodiments, alow-cost but efficient method is described for such protection withoutthe need for external supplemental devices.

Embodiments of the methods and systems disclosed herein may includeprecise voltage reference for A/D zero reference. Some A/D chips providetheir own internal zero voltage reference to be used as a mid-scalevalue for external signal conditioning circuitry to ensure that both theA/D and external op amps use the same reference. Although this soundsreasonable in principle, there are practical complications. In manycases these references are inherently based on a supply voltage using aresistor-divider. For many current systems, especially those whose poweris derived from a PC via USB or similar bus, this provides for anunreliable reference, as the supply voltage will often vary quitesignificantly with load. This is especially true for delta-sigma A/Dchips which necessitate increased signal processing. Although theoffsets may drift together with load, a problem arises if one wants tocalibrate the readings digitally. It is typical to modify the voltageoffset expressed as counts coming from the A/D digitally to compensatefor the DC drift. However, for this case, if the proper calibrationoffset is determined for one set of loading conditions, they will notapply for other conditions. An absolute DC offset expressed in countswill no longer be applicable. As a result, it becomes necessary tocalibrate for all loading conditions which becomes complex, unreliable,and ultimately unmanageable. In embodiments, an external voltagereference is used which is simply independent of the supply voltage touse as the zero offset.

Embodiments of the methods and systems disclosed herein may includephase-lock-loop band pass tracking filter method for obtainingslow-speed RPMs and phase for balancing purposes. For balancingpurposes, it is sometimes necessary to balance at very slow speeds. Atypical tracking filter may be constructed based on a phase-lock loop orPLL design. However, stability and speed range are overriding concerns.In embodiments, a number of digitally controlled switches are used forselecting the appropriate RC and damping constants. The switching can bedone all automatically after measuring the frequency of the incomingtach signal. Embodiments of the methods and systems disclosed herein mayinclude digital derivation of phase relative to input and triggerchannels using on-board timers. In embodiments, digital phase derivationuses digital timers to ascertain an exact delay from a trigger event tothe precise start of data acquisition. This delay, or offset, then, isfurther refined using interpolation methods to obtain an even moreprecise offset which is then applied to the analytically determinedphase of the acquired data such that the phase is “in essence” anabsolute phase with precise mechanical meaning useful for among otherthings, one-shot balancing, alignment analysis, and so on.

Embodiments of the methods and systems disclosed herein may includepeak-detector for auto-scaling routed into separate A/D. Manymicroprocessors in use today feature built-in A/D converters. Forvibration analysis purposes, they are more often than not inadequatewith regards to number of bits, number of channels or sampling frequencyversus not slowing the microprocessor down significantly. Despite theselimitations, it is useful to use them for purposes of auto-scaling. Inembodiments, a separate A/D may be used that has reduced functionalityand is cheaper. For each channel of input, after the signal is buffered(usually with the appropriate coupling: AC or DC) but before it issignal conditioned, the signal is fed directly into the microprocessoror low-cost A/D. Unlike the conditioned signal for which range, gain andfilter switches are thrown, no switches are varied. This can permit thesimultaneous sampling of the auto-scaling data while the input data issignal conditioned, fed into a more robust external A/D, and directedinto on-board memory using direct memory access (DMA) methods wherememory is accessed without requiring a CPU. This significantlysimplifies the auto-scaling process by not having to throw switches andthen allow for settling time, which greatly slows down the auto-scalingprocess. Furthermore, the data can be collected simultaneously, whichassures the best signal-to-noise ratio. The reduced number of bits andother features is usually more than adequate for auto-scaling purposes.

Embodiments of the methods and systems disclosed herein may includerouting of trigger channel either raw or buffered into other analogchannels. Many systems have trigger channels for the purposes ofdetermining relative phase between various input data sets or foracquiring significant data without the needless repetition of unwantedinput. In embodiments, digitally controlled relays are used to switcheither the raw or buffered trigger signal into one of the inputchannels. Many times, it is extremely useful to examine the quality ofthe triggering pulse because it is often corrupted for a variety ofreasons. These reasons include inadequate placement of the triggersensor, wiring issues, faulty setup issues such as a dirty piece ofreflective tape if using an optical sensor, and so on. The ability tolook at either the raw or buffered signal offers an excellent diagnosticor debugging vehicle. It also can offer some improved phase analysiscapability by making use of the recorded data signal for various signalprocessing techniques such as variable speed filtering algorithms.

Embodiments of the methods and systems disclosed herein may includeusing higher input oversampling for delta-sigma A/D for lower samplingrate outputs to minimize AA filter requirements. In embodiments, higherinput oversampling rates for delta-sigma A/D are used for lower samplingrate output data to minimize the AA filtering requirements. Loweroversampling rates can be used for higher sampling rates. For example, a3^(rd) order AA filter set for the lowest sampling requirement for 256Hz (Fmax of 100 Hz) is then adequate for Fmax ranges of 200 and 500 Hz.Another higher-cutoff AA filter can then be used for Fmax ranges from 1kHz and higher (with a secondary filter kicking in at 2.56× the highestsampling rate of 128 kHz). Embodiments of the methods and systemsdisclosed herein may include use of a CPLD as a clock-divider for adelta-sigma A/D to achieve lower sampling rates without the need fordigital resampling. In embodiments, a high-frequency crystal referencecan be divided down to lower frequencies by employing a CPLD as aprogrammable clock divider. The accuracy of the divided down lowerfrequencies is even more accurate than the original source relative totheir longer time periods. This also minimizes or removes the need forresampling processing by the delta-sigma A/D.

Embodiments of the methods and systems disclosed herein may includesignal processing firmware/hardware. In embodiments, long blocks of dataare acquired at high-sampling rate as opposed to multiple sets of datataken at different sampling rates. Typically, in modern route collectionfor vibration analysis, it is customary to collect data at a fixedsampling rate with a specified data length. The sampling rate and datalength may vary from route point to point based on the specificmechanical analysis requirements at hand. For example, a motor mayrequire a relatively low sampling rate with high resolution todistinguish running speed harmonics from line frequency harmonics. Thepractical trade-off here though is that it takes more collection time toachieve this improved resolution. In contrast, some high-speedcompressors or gear sets require much higher sampling rates to measurethe amplitudes of relatively higher frequency data although the preciseresolution may not be as necessary. Ideally, however, it would be betterto collect a very long sample length of data at a very high samplingrate. When digital acquisition devices first started to be popularizedin the early 1980's, the A/D sampling, digital storage, andcomputational abilities were not close to what they are today, socompromises were made between the time required for data collection andthe desired resolution and accuracy. It was because of this limitationthat some analysts in the field even refused to give up their analogtape recording systems, which did not suffer as much from these samedigitizing drawbacks. A few hybrid systems were employed that woulddigitize the play back of the recorded analog data at multiple samplingrates and lengths desired, though these systems were admittedly lessautomated. The more common approach, as mentioned earlier, is to balancedata collection time with analysis capability and digitally acquire thedata blocks at multiple sampling rates and sampling lengths anddigitally store these blocks separately. In embodiments, a long datalength of data can be collected at the highest practical sampling rate(e.g., 102.4 kHz; corresponding to a 40 kHz Fmax) and stored. This longblock of data can be acquired in the same amount of time as the shorterlength of the lower sampling rates utilized by a priori methods so thatthere is no effective delay added to the sampling at the measurementpoint, always a concern in route collection. In embodiments, analog taperecording of data is digitally simulated with such a precision that itcan be in effect considered continuous or “analog” for many purposes,including for purposes of embodiments of the present disclosure, exceptwhere context indicates otherwise.

Embodiments of the methods and systems disclosed herein may includestorage of calibration data and maintenance history on-board card sets.Many data acquisition devices which rely on interfacing to a PC tofunction store their calibration coefficients on the PC. This isespecially true for complex data acquisition devices whose signal pathsare many and therefore whose calibration tables can be quite large. Inembodiments, calibration coefficients are stored in flash memory whichwill remember this data or any other significant information for thatmatter, for all practical purposes, permanently. This information mayinclude nameplate information such as serial numbers of individualcomponents, firmware or software version numbers, maintenance historyand the calibration tables. In embodiments, no matter which computer thebox is ultimately connected to, the DAQ box remains calibrated andcontinues to hold all of this critical information. The PC or externaldevice may poll for this information at any time for implantation orinformation exchange purposes.

Embodiments of the methods and systems disclosed herein may includerapid route creation taking advantage of hierarchical templates. In thefield of vibration monitoring, as well as parametric monitoring ingeneral, it is necessary to establish in a database or functionalequivalent the existence of data monitoring points. These points areassociated a variety of attributes including the following categories:transducer attributes, data collection settings, machinery parametersand operating parameters. The transducer attributes would include probetype, probe mounting type and probe mounting direction or axisorientation. Data collection attributes associated with the measurementwould involve a sampling rate, data length, integrated electronicpiezoelectric probe power and coupling requirements, hardwareintegration requirements, 4-20 or voltage interfacing, range and gainsettings (if applicable), filter requirements, and so on. Machineryparametric requirements relative to the specific point would includesuch items as operating speed, bearing type, bearing parametric datawhich for a rolling element bearing includes the pitch diameter, numberof balls, inner race, and outer-race diameters. For a tilting padbearing, this would include the number of pads and so on. Formeasurement points on a piece of equipment such as a gearbox, neededparameters would include, for example, the number of gear teeth on eachof the gears. For induction motors, it would include the number of rotorbars and poles; for compressors, the number of blades and/or vanes; forfans, the number of blades. For belt/pulley systems, the number of beltsas well as the relevant belt-passing frequencies may be calculated fromthe dimensions of the pulleys and pulley center-to-center distance. Formeasurements near couplings, the coupling type and number of teeth in ageared coupling may be necessary, and so on. Operating parametric datawould include operating load, which may be expressed in megawatts, flow(either air or fluid), percentage, horsepower, feet-per-minute, and soon. Operating temperatures both ambient and operational, pressures,humidity, and so on, may also be relevant. As can be seen, the setupinformation required for an individual measurement point can be quitelarge. It is also crucial to performing any legitimate analysis of thedata. Machinery, equipment and bearing specific information is essentialfor identifying fault frequencies as well as anticipating the variouskinds of specific faults to be expected. The transducer attributes aswell as data collection parameters are vital for properly interpretingthe data along with providing limits for the type of analyticaltechniques suitable. The traditional means of entering this data hasbeen manual and quite tedious, usually at the lowest hierarchical level(for example, at the bearing level with regards to machineryparameters), and at the transducer level for data collection setupinformation. It cannot be stressed enough, however, the importance ofthe hierarchical relationships necessary to organize data both foranalytical and interpretive purposes as well as the storage and movementof data. Here, we are focusing primarily on the storage and movement ofdata. By its nature, the aforementioned setup information is extremelyredundant at the level of the lowest hierarchies. However, because ofits strong hierarchical nature, it can be stored quite efficiently inthat form. In embodiments, hierarchical nature can be utilized whencopying data in the form of templates. As an example, hierarchicalstorage structure suitable for many purposes is defined from general tospecific of company, plant or site, unit or process, machine, equipment,shaft element, bearing, and transducer. It is much easier to copy dataassociated with a particular machine, piece of equipment, shaft elementor bearing than it is to copy only at the lowest transducer level. Inembodiments, the system not only stores data in this hierarchicalfashion, but robustly supports the rapid copying of data using thesehierarchical templates. Similarity of elements at specific hierarchicallevels lends itself to effective data storage in hierarchical format.For example, so many machines have common elements such as motors,gearboxes, compressors, belts, fans, and so on. More specifically, manymotors can be easily classified as induction, DC, fixed or variablespeed. Many gearboxes can be grouped into commonly occurring groupingssuch as input/output, input pinion/intermediate pinion/output pinion,4-posters, and so on. Within a plant or company, there are many similartypes of equipment purchased and standardized on for both cost andmaintenance reasons. This results in an enormous overlapping of similartypes of equipment and, as a result, offers a great opportunity fortaking advantage of a hierarchical template approach.

Embodiments of the methods and systems disclosed herein may includesmart bands. Smart bands refer to any processed signal characteristicsderived from any dynamic input or group of inputs for the purposes ofanalyzing the data and achieving the correct diagnoses. Furthermore,smart bands may even include mini or relatively simple diagnoses for thepurposes of achieving a more robust and complex one. Historically, inthe field of mechanical vibration analysis, Alarm Bands have been usedto define spectral frequency bands of interest for the purposes ofanalyzing and/or trending significant vibration patterns. The Alarm Bandtypically consists of a spectral (amplitude plotted against frequency)region defined between a low and high frequency border. The amplitudebetween these borders is summed in the same manner for which an overallamplitude is calculated. A Smart Band is more flexible in that it notonly refers to a specific frequency band but can also refer to a groupof spectral peaks such as the harmonics of a single peak, a true-peaklevel or crest factor derived from a time waveform, an overall derivedfrom a vibration envelope spectrum or other specialized signal analysistechnique or a logical combination (AND, OR, XOR, etc.) of these signalattributes. In addition, a myriad assortment of other parametric data,including system load, motor voltage and phase information, bearingtemperature, flow rates, and the like, can likewise be used as the basisfor forming additional smart bands. In embodiments, Smart Band symptomsmay be used as building blocks for an expert system whose engine wouldutilize these inputs to derive diagnoses. Some of these mini-diagnosesmay then in turn be used as Smart-Band symptoms (smart bands can includeeven diagnoses) for more generalized diagnoses.

Embodiments of the methods and systems disclosed herein may include aneural net expert system using smart bands. Typical vibration analysisengines are rule-based (i.e. they use a list of expert rules which, whenmet, trigger specific diagnoses). In contrast, a neural approachutilizes the weighted triggering of multiple input stimuli into smalleranalytical engines or neurons which in turn feed a simplified weightedoutput to other neurons. The output of these neurons can be alsoclassified as smart bands which in turn feed other neurons. Thisproduces a more layered approach to expert diagnosing as opposed to theone-shot approach of a rule-based system. In embodiments, the expertsystem utilizes this neural approach using smart bands; however, it doesnot preclude rule-based diagnoses being reclassified as smart bands asfurther stimuli to be utilized by the expert system. From thispoint-of-view, it can be overviewed as a hybrid approach, although atthe highest level it is essentially neural.

Embodiments of the methods and systems disclosed herein may include useof database hierarchy in analysis. smart band symptoms and diagnoses maybe assigned to various hierarchical database levels. For example, asmart band may be called “Looseness” at the bearing level, trigger“Looseness” at the equipment level, and trigger “Looseness” at themachine level. Another example would be having a smart band diagnosiscalled “Horizontal Plane Phase Flip” across a coupling and generate asmart band diagnosis of “Vertical Coupling Misalignment” at the machinelevel.

Embodiments of the methods and systems disclosed herein may includeexpert system GUIs. In embodiments, the system undertakes a graphicalapproach to defining smart bands and diagnoses for the expert system.The entry of symptoms, rules, or more generally smart bands for creatinga particular machine diagnosis, can be tedious and time consuming. Onemeans of making the process more expedient and efficient is to provide agraphical means by use of wiring. The proposed graphical interfaceconsists of four major components: a symptom parts bin, diagnoses bin,tools bin, and graphical wiring area (“GWA”). In embodiments, a symptomparts bin includes various spectral, waveform, envelope and any type ofsignal processing characteristic or grouping of characteristics such asa spectral peak, spectral harmonic, waveform true-peak, waveformcrest-factor, spectral alarm band, and so on. Each part may be assignedadditional properties. For example, a spectral peak part may be assigneda frequency or order (multiple) of running speed. Some parts may bepre-defined or user defined such as a 1×, 2×, 3× running speed, 1×, 2×,3× gear mesh, 1×, 2×, 3× blade pass, number of motor rotor bars×runningspeed, and so on.

In embodiments, a diagnoses bin includes various pre-defined as well asuser-defined diagnoses such as misalignment, imbalance, looseness,bearing faults, and so on. Like parts, diagnoses may also be used asparts for the purposes of building more complex diagnoses. Inembodiments, a tools bin includes logical operations such as AND, OR,XOR, etc. or other ways of combining the various parts listed above suchas Find Max, Find Min, Interpolate, Average, other StatisticalOperations, etc. In embodiments, a graphical wiring area includes partsfrom the parts bin or diagnoses from the diagnoses bin and may becombined using tools to create diagnoses. The various parts, tools anddiagnoses will be represented with icons which are simply graphicallywired together in the desired manner. Embodiments of the methods andsystems disclosed herein may include an expert system GUIs graphicalapproach to defining smart bands and diagnoses for the Expert System.The entry of symptoms, rules or more generally smart bands, for creatinga particular machine diagnosis, can be tedious and time consuming. Onemeans of making the process more expedient and efficient is to provide agraphical means by use of wiring. In embodiments, a graphical interfacemay consist of four major components: a symptom parts bin, diagnosesbin, tools bin and graphical wiring area (“GWA”). The symptom parts binconsists of various spectral, waveform, envelope and any type of signalprocessing characteristic or grouping of characteristics such as aspectral peak, spectral harmonic, waveform true-peak, waveformcrest-factor, spectral alarm band, and so on. Each part may be assignedadditional properties; for example, a spectral peak part may be assigneda frequency or order (multiple) of running speed. Some parts may bepre-defined or user defined such as a 1×, 2×, 3× running speed, 1×, 2×,3× gear mesh, 1×, 2×, 3× blade pass, number of motor rotor bars×runningspeed, and so on. The diagnoses bin consists of various pre-defined aswell as user-defined diagnoses such as misalignment, imbalance,looseness, bearing faults, and so on. Like parts, diagnoses may also beused as parts for the purposes of building more complex diagnoses. Thetools bin consists of logical operations such as AND, OR, XOR, etc., orother ways of combining the various parts listed above such as find fax,find min, interpolate, average, other statistical operations, etc. A GWAmay consist of, in general, parts from the parts bin or diagnoses fromthe diagnoses bin which are wired together using tools to creatediagnoses. The various parts, tools and diagnoses will be representedwith icons, which are simply graphically wired together in the desiredmanor.

Embodiments of the methods and systems disclosed herein may include agraphical approach for back-calculation definition. In embodiments, theexpert system also provides the opportunity for the system to learn. Ifone already knows that a unique set of stimuli or smart bandscorresponds to a specific fault or diagnosis, then it is possible toback-calculate a set of coefficients that when applied to a future setof similar stimuli would arrive at the same diagnosis. In embodiments,if there are multiple sets of data a best-fit approach may be used.Unlike the smart band GUI, this embodiment will self-generate a wiringdiagram. In embodiments, the user may tailor the back-propagationapproach settings and use a database browser to match specific sets ofdata with the desired diagnoses. In embodiments, the desired diagnosesmay be created or custom tailored with a smart band GUI. In embodiments,after that, a user may press the GENERATE button and a dynamic wiring ofthe symptom-to-diagnosis may appear on the screen as it works throughthe algorithms to achieve the best fit. In embodiments, when complete, avariety of statistics are presented which detail how well the mappingprocess proceeded. In some cases, no mapping may be achieved if, forexample, the input data was all zero or the wrong data (mistakenlyassigned) and so on. Embodiments of the methods and systems disclosedherein may include bearing analysis methods. In embodiments, bearinganalysis methods may be used in conjunction with a computer aided design(“CAD”), predictive deconvolution, minimum variance distortionlessresponse (“MVDR”) and spectrum sum-of-harmonics.

Embodiments of the methods and systems disclosed herein may includetorsional vibration detection and analysis utilizing transitory signalanalysis. There has been a marked trend in recent times regarding theprevalence of variable speed machinery. Due primarily to the decrease incost of motor speed control systems, as well as the increased cost andconsciousness of energy-usage, it has become more economicallyjustifiable to take advantage of the potentially vast energy savings ofload control. Unfortunately, one frequently overlooked design aspect ofthis issue is that of vibration. When a machine is designed to run atonly one speed, it is far easier to design the physical structureaccordingly so as to avoid mechanical resonances both structural andtorsional, each of which can dramatically shorten the mechanical healthof a machine. This would include such structural characteristics as thetypes of materials to use, their weight, stiffening member requirementsand placement, bearing types, bearing location, base supportconstraints, etc. Even with machines running at one speed, designing astructure so as to minimize vibration can prove a daunting task,potentially requiring computer modeling, finite-element analysis, andfield testing. By throwing variable speeds into the mix, in many cases,it becomes impossible to design for all desirable speeds. The problemthen becomes one of minimization, e.g., by speed avoidance. This is whymany modern motor controllers are typically programmed to skip orquickly pass through specific speed ranges or bands. Embodiments mayinclude identifying speed ranges in a vibration monitoring system.Non-torsional, structural resonances are typically fairly easy to detectusing conventional vibration analysis techniques. However, this is notthe case for torsion. One special area of current interest is theincreased incidence of torsional resonance problems, apparently due tothe increased torsional stresses of speed change as well as theoperation of equipment at torsional resonance speeds. Unlikenon-torsional structural resonances which generally manifest theireffect with dramatically increased casing or external vibration,torsional resonances generally show no such effect. In the case of ashaft torsional resonance, the twisting motion induced by the resonancemay only be discernible by looking for speed and/or phase changes. Thecurrent standard methodology for analyzing torsional vibration involvesthe use of specialized instrumentation. Methods and systems disclosedherein allow analysis of torsional vibration without such specializedinstrumentation. This may consist of shutting the machine down andemploying the use of strain gauges and/or other special fixturing suchas speed encoder plates and/or gears. Friction wheels are anotheralternative but they typically require manual implementation and aspecialized analyst. In general, these techniques can be prohibitivelyexpensive and/or inconvenient. An increasing prevalence of continuousvibration monitoring systems due to decreasing costs and increasingconvenience (e.g., remote access) exists. In embodiments, there is anability to discern torsional speed and/or phase variations with just thevibration signal. In embodiments, transient analysis techniques may beutilized to distinguish torsionally induced vibrations from mere speedchanges due to process control. In embodiments, factors for discernmentmight focus on one or more of the following aspects: the rate of speedchange due to variable speed motor control would be relatively slow,sustained and deliberate; torsional speed changes would tend to beshort, impulsive and not sustained; torsional speed changes would tendto be oscillatory, most likely decaying exponentially, process speedchanges would not; and smaller speed changes associated with torsionrelative to the shaft's rotational speed which suggest that monitoringphase behavior would show the quick or transient speed bursts incontrast to the slow phase changes historically associated with rampinga machine's speed up or down (as typified with Bode or Nyquist plots).

Embodiments of the methods and systems disclosed herein may includeimproved integration using both analog and digital methods. When asignal is digitally integrated using software, essentially the spectrallow-end frequency data has its amplitude multiplied by a function whichquickly blows up as it approaches zero and creates what is known in theindustry as a “ski-slope” effect. The amplitude of the ski-slope isessentially the noise floor of the instrument. The simple remedy forthis is the traditional hardware integrator, which can perform atsignal-to-noise ratios much greater than that of an already digitizedsignal. It can also limit the amplification factor to a reasonable levelso that multiplication by very large numbers is essentially prohibited.However, at high frequencies where the frequency becomes large, theoriginal amplitude which may be well above the noise floor is multipliedby a very small number (1/f) that plunges it well below the noise floor.The hardware integrator has a fixed noise floor that although low floordoes not scale down with the now lower amplitude high-frequency data. Incontrast, the same digital multiplication of a digitized high-frequencysignal also scales down the noise floor proportionally. In embodiments,hardware integration may be used below the point of unity gain where (ata value usually determined by units and/or desired signal to noise ratiobased on gain) and software integration may be used above the value ofunity gain to produce an ideal result. In embodiments, this integrationis performed in the frequency domain. In embodiments, the resultinghybrid data can then be transformed back into a waveform which should befar superior in signal-to-noise ratio when compared to either hardwareintegrated or software integrated data. In embodiments, the strengths ofhardware integration are used in conjunction with those of digitalsoftware integration to achieve the maximum signal-to-noise ratio. Inembodiments, the first order gradual hardware integrator high passfilter along with curve fitting allow some relatively low frequency datato get through while reducing or eliminating the noise, allowing veryuseful analytical data that steep filters kill to be salvaged.

Embodiments of the methods and systems disclosed herein may includeadaptive scheduling techniques for continuous monitoring. Continuousmonitoring is often performed with an up-front Mux whose purpose it isto select a few channels of data among many to feed the hardware signalprocessing, A/D, and processing components of a DAQ system. This is doneprimarily out of practical cost considerations. The tradeoff is that allof the points are not monitored continuously (although they may bemonitored to a lesser extent via alternative hardware methods). Inembodiments, multiple scheduling levels are provided. In embodiments, atthe lowest level, which is continuous for the most part, all of themeasurement points will be cycled through in round-robin fashion. Forexample, if it takes 30 seconds to acquire and process a measurementpoint and there are 30 points, then each point is serviced once every 15minutes. However, if a point should alarm by whatever criteria the userselects, its priority level can be increased so that it is serviced moreoften. As there can be multiple grades of severity for each alarm, socan there me multiple levels of priority with regards to monitoring. Inembodiments, more severe alarms will be monitored more frequently. Inembodiments, a number of additional high-level signal processingtechniques can be applied at less frequent intervals. Embodiments maytake advantage of the increased processing power of a PC and the PC cantemporarily suspend the round-robin route collection (with its multipletiers of collection) process and stream the required amount of data fora point of its choosing. Embodiments may include various advancedprocessing techniques such as envelope processing, wavelet analysis, aswell as many other signal processing techniques. In embodiments, afteracquisition of this data, the DAQ card set will continue with its routeat the point it was interrupted. In embodiments, various PC scheduleddata acquisitions will follow their own schedules which will be lessfrequency than the DAQ card route. They may be set up hourly, daily, bynumber of route cycles (for example, once every 10 cycles) and alsoincreased scheduling-wise based on their alarm severity priority or typeof measurement (e.g., motors may be monitored differently than fans.

Embodiments of the methods and systems disclosed herein may include dataacquisition parking features. In embodiments, a data acquisition boxused for route collection, real time analysis and in general as anacquisition instrument can be detached from its PC (tablet or otherwise)and powered by an external power supply or suitable battery. Inembodiments, the data collector still retains continuous monitoringcapability and its on-board firmware can implement dedicated monitoringfunctions for an extended period of time or can be controlled remotelyfor further analysis. Embodiments of the methods and systems disclosedherein may include extended statistical capabilities for continuousmonitoring.

Embodiments of the methods and systems disclosed herein may includeambient sensing plus local sensing plus vibration for analysis. Inembodiments, ambient environmental temperature and pressure, sensedtemperature and pressure may be combined with long/medium term vibrationanalysis for prediction of any of a range of conditions orcharacteristics. Variants may add infrared sensing, infraredthermography, ultrasound, and many other types of sensors and inputtypes in combination with vibration or with each other. Embodiments ofthe methods and systems disclosed herein may include a smart route. Inembodiments, the continuous monitoring system's software willadapt/adjust the data collection sequence based on statistics,analytics, data alarms and dynamic analysis. Typically, the route is setbased on the channels the sensors are attached to. In embodiments, withthe crosspoint switch, the Mux can combine any input Mux channels to the(e.g., eight) output channels. In embodiments, as channels go into alarmor the system identifies key deviations, it will pause the normal routeset in the software to gather specific simultaneous data, from thechannels sharing key statistical changes, for more advanced analysis.Embodiments include conducting a smart ODS or smart transfer function.

Embodiments of the methods and systems disclosed herein may includesmart ODS and one or more transfer functions. In embodiments, due to asystem's multiplexer and crosspoint switch, an ODS, a transfer function,or other special tests on all the vibration sensors attached to amachine/structure can be performed and show exactly how the machine'spoints are moving in relationship to each other. In embodiments, 40-50kHz and longer data lengths (e.g., at least one minute) may be streamed,which may reveal different information than what a normal ODS ortransfer function will show. In embodiments, the system will be able todetermine, based on the data/statistics/analytics to use, the smartroute feature that breaks from the standard route and conducts an ODSacross a machine, structure or multiple machines and structures thatmight show a correlation because the conditions/data directs it. Inembodiments, for the transfer functions there may be an impact hammerused on one channel and compared against other vibration sensors on themachine. In embodiments, the system may use the condition changes suchas load, speed, temperature or other changes in the machine or system toconduct the transfer function. In embodiments, different transferfunctions may be compared to each other over time. In embodiments,difference transfer functions may be strung together like a movie thatmay show how the machinery fault changes, such as a bearing that couldshow how it moves through the four stages of bearing failure and so on.Embodiments of the methods and systems disclosed herein may include ahierarchical Mux. In embodiments, a hierarchical Mux may allow modularlyoutput of more channels, such as 16, 24 or more to multiple of eightchannel card sets, which would allow gathering more simultaneouschannels of data for more complex analysis and faster data collection.Methods and systems are disclosed herein for continuous ultrasonicmonitoring, including providing continuous ultrasonic monitoring ofrotating elements and bearings of an energy production facility.

With reference to FIG. 8, the present disclosure generally includesdigitally collecting or streaming waveform data 2010 from a machine 2020whose operational speed can vary from relatively slow rotational oroscillational speeds to much higher speeds in different situations. Thewaveform data 2010, at least on one machine, may include data from asingle axis sensor 2030 mounted at an unchanging reference location 2040and from a three-axis sensor 2050 mounted at changing locations (orlocated at multiple locations), including location 2052. In embodiments,the waveform data 2010 can be vibration data obtained simultaneouslyfrom each sensor 2030, 2050 in a gap-free format for a duration ofmultiple minutes with maximum resolvable frequencies sufficiently largeto capture periodic and transient impact events. By way of this example,the waveform data 2010 can include vibration data that can be used tocreate an operational deflecting shape. It can also be used, as needed,to diagnose vibrations from which a machine repair solution can beprescribed.

In embodiments, the machine 2020 can further include a housing 2100 thatcan contain a drive motor 2110 that can drive a shaft 2120. The shaft2120 can be supported for rotation or oscillation by a set of bearings2130, such as including a first bearing 2140 and a second bearing 2150.A data collection module 2160 can connect to (or be resident on) themachine 2020. In one example, the data collection module 2160 can belocated and accessible through a cloud network facility 2170, cancollect the waveform data 2010 from the machine 2020, and deliver thewaveform data 2010 to a remote location. A working end 2180 of the driveshaft 2120 of the machine 2020 can drive a windmill, a fan, a pump, adrill, a gear system, a drive system, or other working element, as thetechniques described herein can apply to a wide range of machines,equipment, tools, or the like that include rotating or oscillatingelements. In other instances, a generator can be substituted for themotor 2110, and the working end of the drive shaft 2120 can directrotational energy to the generator to generate power, rather thanconsume it.

In embodiments, the waveform data 2010 can be obtained using apredetermined route format based on the layout of the machine 2020. Thewaveform data 2010 may include data from the single axis sensor 2030 andthe three-axis sensor 2050. The single-axis sensor 2030 can serve as areference probe with its one channel of data and can be fixed at theunchanging location 2040 on the machine under survey. The three-axissensor 2050 can serve as a tri-axial probe (e.g., three orthogonal axes)with its three channels of data and can be moved along a predetermineddiagnostic route format from one test point to the next test point. Inone example, both sensors 2030, 2050 can be mounted manually to themachine 2020 and can connect to a separate portable computer in certainservice examples. The reference probe can remain at one location whilethe user can move the tri-axial vibration probe along the predeterminedroute, such as from bearing-to-bearing on a machine. In this example,the user is instructed to locate the sensors at the predeterminedlocations to complete the survey (or portion thereof) of the machine.

With reference to FIG. 9, a portion of an exemplary machine 2200 isshown having a tri-axial sensor 2210 mounted to a location 2220associated with a motor bearing of the machine 2200 with an output shaft2230 and output member 2240 in accordance with the present disclosure.With reference to FIG. 9 and FIG. 10, an exemplary machine 2300 is shownhaving a tri-axial sensor 2310 and a single-axis vibration sensor 2320serving as the reference sensor that is attached on the machine 2300 atan unchanging location for the duration of the vibration survey inaccordance with the present disclosure. The tri-axial sensor 2310 andthe single-axis vibration sensor 2320 can be connected to a datacollection system 2330

In further examples, the sensors and data acquisition modules andequipment can be integral to, or resident on, the rotating machine. Byway of these examples, the machine can contain many single axis sensorsand many tri-axial sensors at predetermined locations. The sensors canbe originally installed equipment and provided by the original equipmentmanufacturer or installed at a different time in a retrofit application.The data collection module 2160, or the like, can select and use onesingle axis sensor and obtain data from it exclusively during thecollection of waveform data 2010 while moving to each of the tri-axialsensors. The data collection module 2160 can be resident on the machine2020 and/or connect via the cloud network facility 2170

With reference to FIG. 8, the various embodiments include collecting thewaveform data 2010 by digitally recording locally, or streaming over,the cloud network facility 2170. The waveform data 2010 can be collectedso as to be gap-free with no interruptions and, in some respects, can besimilar to an analog recording of waveform data. The waveform data 2010from all of the channels can be collected for one to two minutesdepending on the rotating or oscillating speed of the machine beingmonitored. In embodiments, the data sampling rate can be at a relativelyhigh sampling rate relative to the operating frequency of the machine2020.

In embodiments, a second reference sensor can be used, and a fifthchannel of data can be collected. As such, the single-axis sensor can bethe first channel and tri-axial vibration can occupy the second, thethird, and the fourth data channels. This second reference sensor, likethe first, can be a single axis sensor, such as an accelerometer. Inembodiments, the second reference sensor, like the first referencesensor, can remain in the same location on the machine for the entirevibration survey on that machine. The location of the first referencesensor (i.e., the single axis sensor) may be different than the locationof the second reference sensors (i.e., another single axis sensor). Incertain examples, the second reference sensor can be used when themachine has two shafts with different operating speeds, with the tworeference sensors being located on the two different shafts. Inaccordance with this example, further single-axis reference sensors canbe employed at additional but different unchanging locations associatedwith the rotating machine.

In embodiments, the waveform data can be transmitted electronically in agap-free free format at a significantly high rate of sampling for arelatively longer period of time. In one example, the period of time is60 seconds to 120 seconds. In another example, the rate of sampling is100 kHz with a maximum resolvable frequency (Fmax) of 40 kHz. It will beappreciated in light of this disclosure that the waveform data can beshown to approximate more closely some of the wealth of data availablefrom previous instances of analog recording of waveform data.

In embodiments, sampling, band selection, and filtering techniques canpermit one or more portions of a long stream of data (i.e., one to twominutes in duration) to be under sampled or over sampled to realizevarying effective sampling rates. To this end, interpolation anddecimation can be used to further realize varying effective samplingrates. For example, oversampling may be applied to frequency bands thatare proximal to rotational or oscillational operating speeds of thesampled machine, or to harmonics thereof, as vibration effects may tendto be more pronounced at those frequencies across the operating range ofthe machine. In embodiments, the digitally-sampled data set can bedecimated to produce a lower sampling rate. It will be appreciated inlight of the disclosure that decimate in this context can be theopposite of interpolate. In embodiments, decimating the data set caninclude first applying a low-pass filter to the digitally-sampled dataset and then undersampling the data set.

In one example, a sample waveform at 100 Hz can be undersampled at everytenth point of the digital waveform to produce an effective samplingrate of 10 Hz, but the remaining nine points of that portion of thewaveform are effectively discarded and not included in the modeling ofthe sample waveform. Moreover, this type of bare undersampling cancreate ghost frequencies due to the undersampling rate (i.e., 10 Hz)relative to the 100 Hz sample waveform.

Most hardware for analog to digital conversions use a sample-and-holdcircuit that can charge up a capacitor for a given amount of time suchthat an average value of the waveform is determined over a specificchange in time. It will be appreciated in light of the disclosure thatthe value of the waveform over the specific change in time in not linearbut more similar to a cardinal sinusoidal function; and, therefore, itcan be shown that more emphasis can be placed on the waveform data atthe center of the sampling interval with exponential decay of thecardinal sinusoidal signal occurring from its center.

By way of the above example, the sample waveform at 100 Hz can behardware-sampled at 10 Hz and therefore each sampling point is averagedover 100 milliseconds (e.g., a signal sampled at 100 Hz can have eachpoint averaged over 10 milliseconds). In contrast to the effectivediscarding of nine out of the ten data points of the sampled waveform asdiscussed above, the present disclosure can include weighing adjacentdata. The adjacent data can include refers to the sample points thatwere previously discarded and the one remaining point that was retained.In one example, a low pass filter can average the adjacent sample datalinearly, i.e., determining the sum of every ten points and thendividing that sum by ten. In a further example, the adjacent data can beweighted with a sinc function. The process of weighting the originalwaveform with the sinc function can be referred to as an impulsefunction, or can be referred to in the time domain as a convolution.

The present disclosure can be applicable to not only digitizing awaveform signal based on a detected voltage, but can also be applicableto digitizing waveform signals based on current waveforms, vibrationwaveforms, and image processing signals including video signalrasterization. In one example, the resizing of a window on a computerscreen can be decimated, albeit in at least two directions. In thesefurther examples, it will be appreciated that undersampling by itselfcan be shown to be insufficient. To that end, oversampling or upsamplingby itself can similarly be shown to be insufficient, such thatinterpolation can be used like decimation but in lieu of onlyundersampling by itself.

It will be appreciated in light of the disclosure that interpolation inthis context can refer to first applying a low pass filter to thedigitally-sampled waveform data and then upsampling the waveform data.It will be appreciated in light of the disclosure that real-worldexamples can often require the use of use non-integer factors fordecimation or interpolation, or both. To that end, the presentdisclosure includes interpolating and decimating sequentially in orderto realize a non-integer factor rate for interpolating and decimating.In one example, interpolating and decimating sequentially can defineapplying a low-pass filter to the sample waveform, then interpolatingthe waveform after the low-pass filter, and then decimating the waveformafter the interpolation. In embodiments, the vibration data can belooped to purposely emulate conventional tape recorder loops, withdigital filtering techniques used with the effective splice tofacilitate longer analyses. It will be appreciated in light of thedisclosure that the above techniques do not preclude waveform, spectrum,and other types of analyses to be processed and displayed with a GUI ofthe user at the time of collection. It will be appreciated in light ofthe disclosure that newer systems can permit this functionality to beperformed in parallel to the high-performance collection of the rawwaveform data.

With respect to time of collection issues, it will be appreciated thatolder systems using the compromised approach of improving dataresolution, by collecting at different sampling rates and data lengths,do not in fact save as much time as expected. To that end, every timethe data acquisition hardware is stopped and started, latency issues canbe created, especially when there is hardware auto-scaling performed.The same can be true with respect to data retrieval of the routeinformation (i.e., test locations) that is often in a database formatand can be exceedingly slow. The storage of the raw data in bursts todisk (whether solid state or otherwise) can also be undesirably slow.

In contrast, the many embodiments include digitally streaming thewaveform data 2010, as disclosed herein and also enjoying the benefit ofneeding to load the route parameter information while setting the dataacquisition hardware only once. Because the waveform data 2010 isstreamed to only one file, there is no need to open and close files, orswitch between loading and writing operations with the storage medium.It can be shown that the collection and storage of the waveform data2010, as described herein, can be shown to produce relatively moremeaningful data in significantly less time than the traditional batchdata acquisition approach. An example of this includes an electric motorabout which waveform data can be collected with a data length of 4Kpoints (i.e., 4,096) for sufficiently high resolution in order to, amongother things, distinguish electrical sideband frequencies. For fans orblowers, a reduced resolution of 1K (i.e., 1,024) can be used. Incertain instances, 1K can be the minimum waveform data lengthrequirement. The sampling rate can be 1,280 Hz and that equates to anFmax of 500 Hz. It will be appreciated in light of the disclosure thatoversampling by an industry standard factor of 2.56 can satisfy thenecessary two-times (2×) oversampling for the Nyquist Criterion withsome additional leeway that can accommodate anti-aliasingfilter-rolloff. The time to acquire this waveform data would be 1,024points at 1,280 hertz, which are 800 milliseconds.

To improve accuracy, the waveform data can be averaged. Eight averagescan be used with, for example, fifty percent overlap. This would extendthe time from 800 milliseconds to 3.6 seconds, which is equal to 800msec×8 averages×0.5 (overlap ratio)+0.5×800 msec (non-overlapped headand tail ends). After collection at Fmax=500 Hz waveform data, a highersampling rate can be used. In one example, ten times (10×) the previoussampling rate can be used and Fmax=10 kHz. By way of this example, eightaverages can be used with fifty percent (50%) overlap to collectwaveform data at this higher rate that can amount to a collection timeof 360 msec or 0.36 seconds. It will be appreciated in light of thedisclosure that it can be necessary to read the hardware collectionparameters for the higher sampling rate from the route list, as well aspermit hardware auto-scaling, or the resetting of other necessaryhardware collection parameters, or both. To that end, a few seconds oflatency can be added to accommodate the changes in sampling rate. Inother instances, introducing latency can accommodate hardwareautoscaling and changes to hardware collection parameters that can berequired when using the lower sampling rate disclosed herein. Inaddition to accommodating the change in sampling rate, additional timeis needed for reading the route point information from the database(i.e., where to monitor and where to monitor next), displaying the routeinformation, and processing the waveform data. Moreover, display of thewaveform data and/or associated spectra can also consume significanttime. In light of the above, 15 seconds to 20 seconds can elapse whileobtaining waveform data at each measurement point.

In further examples, additional sampling rates can be added but this canmake the total amount time for the vibration survey even longer becausetime adds up from changeover time from one sampling rate to another andfrom the time to obtain additional data at different sampling rate. Inone example, a lower sampling rate is used, such as a sampling rate of128 Hz where Fmax=50 Hz. By way of this example, the vibration surveywould therefore require an additional 36 seconds for the first set ofaveraged data at this sampling rate, in addition to others mentionedabove, and consequently the total time spent at each measurement pointincreases even more dramatically. Further embodiments include usingsimilar digital streaming of gap free waveform data as disclosed hereinfor use with wind turbines and other machines that can have relativelyslow speed rotating or oscillating systems. In many examples, thewaveform data collected can include long samples of data at a relativelyhigh sampling rate. In one example, the sampling rate can be 100 kHz andthe sampling duration can be for two minutes on all of the channelsbeing recorded. In many examples, one channel can be for the single axisreference sensor and three more data channels can be for the tri-axialthree channel sensor. It will be appreciated in light of the disclosurethat the long data length can be shown to facilitate detection ofextremely low frequency phenomena. The long data length can also beshown to accommodate the inherent speed variability in wind turbineoperations. Additionally, the long data length can further be shown toprovide the opportunity for using numerous averages such as thosediscussed herein, to achieve very high spectral resolution, and to makefeasible tape loops for certain spectral analyses. Many multipleadvanced analytical techniques can now become available because suchtechniques can use the available long uninterrupted length of waveformdata in accordance with the present disclosure.

It will also be appreciated in light of the disclosure that thesimultaneous collection of waveform data from multiple channels canfacilitate performing transfer functions between multiple channels.Moreover, the simultaneous collection of waveform data from multiplechannels facilitates establishing phase relationships across the machineso that more sophisticated correlations can be utilized by relying onthe fact that the waveforms from each of the channels are collectedsimultaneously. In other examples, more channels in the data collectioncan be used to reduce the time it takes to complete the overallvibration survey by allowing for simultaneous acquisition of waveformdata from multiple sensors that otherwise would have to be acquired, ina subsequent fashion, moving sensor to sensor in the vibration survey.

The present disclosure includes the use of at least one of thesingle-axis reference probe on one of the channels to allow foracquisition of relative phase comparisons between channels. Thereference probe can be an accelerometer or other type of transducer thatis not moved and, therefore, fixed at an unchanging location during thevibration survey of one machine. Multiple reference probes can each bedeployed as at suitable locations fixed in place (i.e., at unchanginglocations) throughout the acquisition of vibration data during thevibration survey. In certain examples, up to seven reference probes canbe deployed depending on the capacity of the data collection module 2160or the like. Using transfer functions or similar techniques, therelative phases of all channels may be compared with one another at allselected frequencies. By keeping the one or more reference probes fixedat their unchanging locations while moving or monitoring the othertri-axial vibration sensors, it can be shown that the entire machine canbe mapped with regard to amplitude and relative phase. This can be shownto be true even when there are more measurement points than channels ofdata collection. With this information, an operating deflection shapecan be created that can show dynamic movements of the machine in 3 D,which can provide an invaluable diagnostic tool. In embodiments, the oneor more reference probes can provide relative phase, rather thanabsolute phase. It will be appreciated in light of the disclosure thatrelative phase may not be as valuable absolute phase for some purposes,but the relative phase the information can still be shown to be veryuseful.

In embodiments, the sampling rates used during the vibration survey canbe digitally synchronized to predetermined operational frequencies thatcan relate to pertinent parameters of the machine such as rotating oroscillating speed. Doing this, can permit extracting even moreinformation using synchronized averaging techniques. It will beappreciated in light of the disclosure that this can be done without theuse of a key phasor or a reference pulse from a rotating shaft, which isusually not available for route collected data. As such, non-synchronoussignals can be removed from a complex signal without the need to deploysynchronous averaging using the key phasor. This can be shown to be verypowerful when analyzing a particular pinon in a gearbox or generallyapplied to any component within a complicated mechanical mechanism. Inmany instances, the key phasor or the reference pulse is rarelyavailable with route collected data, but the techniques disclosed hereincan overcome this absence. In embodiments, there can be multiple shaftsrunning at different speeds within the machine being analyzed. Incertain instances, there can be a single-axis reference probe for eachshaft. In other instances, it is possible to relate the phase of oneshaft to another shaft using only one single axis reference probe on oneshaft at its unchanging location. In embodiments, variable speedequipment can be more readily analyzed with relatively longer durationof data relative to single speed equipment. The vibration survey can beconducted at several machine speeds within the same contiguous set ofvibration data using the same techniques disclosed herein. Thesetechniques can also permit the study of the change of the relationshipbetween vibration and the change of the rate of speed that was notavailable before.

In embodiments, there are numerous analytical techniques that can emergefrom because raw waveform data can be captured in a gap-free digitalformat as disclosed herein. The gap-free digital format can facilitatemany paths to analyze the waveform data in many ways after the fact toidentify specific problems. The vibration data collected in accordancewith the techniques disclosed herein can provide the analysis oftransient, semi-periodic and very low frequency phenomena. The waveformdata acquired in accordance with the present disclosure can containrelatively longer streams of raw gap-free waveform data that can beconveniently played back as needed, and on which many and variedsophisticated analytical techniques can be performed. A large number ofsuch techniques can provide for various forms of filtering to extractlow amplitude modulations from transient impact data that can beincluded in the relatively longer stream of raw gap-free waveform data.It will be appreciated in light of the disclosure that in past datacollection practices, these types of phenomena were typically lost bythe averaging process of the spectral processing algorithms because thegoal of the previous data acquisition module was purely periodicsignals; or these phenomena were lost to file size reductionmethodologies due to the fact that much of the content from an originalraw signal was typically discarded knowing it would not be used.

In embodiments, there is a method of monitoring vibration of a machinehaving at least one shaft supported by a set of bearings. The methodincludes monitoring a first data channel assigned to a single-axissensor at an unchanging location associated with the machine. The methodalso includes monitoring a second, third, and fourth data channelassigned to a three-axis sensor. The method further includes recordinggap-free digital waveform data simultaneously from all of the datachannels while the machine is in operation; and determining a change inrelative phase based on the digital waveform data. The method alsoincludes the tri-axial sensor being located at a plurality of positionsassociated with the machine while obtaining the digital waveform. Inembodiments, the second, third, and fourth channels are assignedtogether to a sequence of tri-axial sensors each located at differentpositions associated with the machine. In embodiments, the data isreceived from all of the sensors on all of their channelssimultaneously.

The method also includes determining an operating deflection shape basedon the change in relative phase information and the waveform data. Inembodiments, the unchanging location of the reference sensor is aposition associated with a shaft of the machine. In embodiments, thetri-axial sensors in the sequence of the tri-axial sensors are eachlocated at different positions and are each associated with differentbearings in the machine. In embodiments, the unchanging location is aposition associated with a shaft of the machine and, wherein, thetri-axial sensors in the sequence of the tri-axial sensors are eachlocated at different positions and are each associated with differentbearings that support the shaft in the machine. The various embodimentsinclude methods of sequentially monitoring vibration or similar processparameters and signals of a rotating or oscillating machine or analogousprocess machinery from a number of channels simultaneously, which can beknown as an ensemble. In various examples, the ensemble can include oneto eight channels. In further examples, an ensemble can represent alogical measurement grouping on the equipment being monitored whetherthose measurement locations are temporary for measurement, supplied bythe original equipment manufacturer, retrofit at a later date, or one ormore combinations thereof.

In one example, an ensemble can monitor bearing vibration in a singledirection. In a further example, an ensemble can monitor three differentdirections (e.g., orthogonal directions) using a tri-axial sensor. Inyet further examples, an ensemble can monitor four or more channelswhere the first channel can monitor a single axis vibration sensor, andthe second, the third, and the fourth channels can monitor each of thethree directions of the tri-axial sensor. In other examples, theensemble can be fixed to a group of adjacent bearings on the same pieceof equipment or an associated shaft. The various embodiments providemethods that include strategies for collecting waveform data fromvarious ensembles deployed in vibration studies or the like in arelatively more efficient manner. The methods also includesimultaneously monitoring of a reference channel assigned to anunchanging reference location associated with the ensemble monitoringthe machine. The cooperation with the reference channel can be shown tosupport a more complete correlation of the collected waveforms from theensembles. The reference sensor on the reference channel can be a singleaxis vibration sensor, or a phase reference sensor that can be triggeredby a reference location on a rotating shaft or the like. As disclosedherein, the methods can further include recording gap-free digitalwaveform data simultaneously from all of the channels of each ensembleat a relatively high rate of sampling so as to include all frequenciesdeemed necessary for the proper analysis of the machinery beingmonitored while it is in operation. The data from the ensembles can bestreamed gap-free to a storage medium for subsequent processing that canbe connected to a cloud network facility, a local data link, Bluetoothconnectivity, cellular data connectivity, or the like.

In embodiments, the methods disclosed herein include strategies forcollecting data from the various ensembles including digital signalprocessing techniques that can be subsequently applied to data from theensembles to emphasize or better isolate specific frequencies orwaveform phenomena. This can be in contrast with current methods thatcollect multiple sets of data at different sampling rates, or withdifferent hardware filtering configurations including integration, thatprovide relatively less post-processing flexibility because of thecommitment to these same (known as a priori hardware configurations).These same hardware configurations can also be shown to increase time ofthe vibration survey due to the latency delays associated withconfiguring the hardware for each independent test. In embodiments, themethods for collecting data from various ensembles include data markertechnology that can be used for classifying sections of streamed data ashomogenous and belonging to a specific ensemble. In one example, aclassification can be defined as operating speed. In doing so, amultitude of ensembles can be created from what conventional systemswould collect as only one. The many embodiments include post-processinganalytic techniques for comparing the relative phases of all thefrequencies of interest not only between each channel of the collectedensemble but also between all of the channels of all of the ensemblesbeing monitored, when applicable.

With reference to FIG. 12, the many embodiments include a first machine2400 having rotating or oscillating components 2410, or both, eachsupported by a set of bearings 2420 including a bearing pack 2422, abearing pack 2424, a bearing pack 2426, and more as needed. The firstmachine 2400 can be monitored by a first sensor ensemble 2450. The firstensemble 2450 can be configured to receive signals from sensorsoriginally installed (or added later) on the first machine 2400. Thesensors on the machine 2400 can include single-axis sensors 2460, suchas a single-axis sensor 2462, a single-axis sensor 2464, and more asneeded. In many examples, the single axis-sensors 2460 can be positionedin the machine 2400 at locations that allow for the sensing of one ofthe rotating or oscillating components 2410 of the machine 2400.

The machine 2400 can also have tri-axial (e.g., orthogonal axes) sensors2480, such as a tri-axial sensor 2482, a tri-axial sensor 2484, and moreas needed. In many examples, the tri-axial sensors 2480 can bepositioned in the machine 2400 at locations that allow for the sensingof one of each of the bearing packs in the sets of bearings 2420 that isassociated with the rotating or oscillating components of the machine2400. The machine 2400 can also have temperature sensors 2500, such as atemperature sensor 2502, a temperature sensor 2504, and more as needed.The machine 2400 can also have a tachometer sensor 2510 or more asneeded that each detail the RPMs of one of its rotating components. Byway of the above example, the first sensor ensemble 2450 can survey theabove sensors associated with the first machine 2400. To that end, thefirst ensemble 2450 can be configured to receive eight channels. Inother examples, the first sensor ensemble 2450 can be configured to havemore than eight channels, or less than eight channels as needed. In thisexample, the eight channels include two channels that can each monitor asingle-axis reference sensor signal and three channels that can monitora tri-axial sensor signal. The remaining three channels can monitor twotemperature signals and a signal from a tachometer. In one example, thefirst ensemble 2450 can monitor the single-axis sensor 2462, thesingle-axis sensor 2464, the tri-axial sensor 2482, the temperaturesensor 2502, the temperature sensor 2504, and the tachometer sensor 2510in accordance with the present disclosure. During a vibration survey onthe machine 2400, the first ensemble 2450 can first monitor thetri-axial sensor 2482 and then move next to the tri-axial sensor 2484.

After monitoring the tri-axial sensor 2484, the first ensemble 2450 canmonitor additional tri-axial sensors on the machine 2400 as needed andthat are part of the predetermined route list associated with thevibration survey of the machine 2400, in accordance with the presentdisclosure. During this vibration survey, the first ensemble 2450 cancontinually monitor the single-axis sensor 2462, the single-axis sensor2464, the two temperature sensors 2502, 2504, and the tachometer sensor2510 while the first ensemble 2450 can serially monitor the multipletri-axial sensors 2480 in the pre-determined route plan for thisvibration survey.

With reference to FIG. 12, the many embodiments include a second machine2600 having rotating or oscillating components 2610, or both, eachsupported by a set of bearings 2620 including a bearing pack 2622, abearing pack 2624, a bearing pack 2626, and more as needed. The secondmachine 2600 can be monitored by a second sensor ensemble 2650. Thesecond ensemble 2650 can be configured to receive signals from sensorsoriginally installed (or added later) on the second machine 2600. Thesensors on the machine 2600 can include single-axis sensors 2660, suchas a single-axis sensor 2662, a single-axis sensor 2664, and more asneeded. In many examples, the single axis-sensors 2660 can be positionedin the machine 2600 at locations that allow for the sensing of one ofthe rotating or oscillating components 2610 of the machine 2600.

The machine 2600 can also have tri-axial (e.g., orthogonal axes) sensors2680, such as a tri-axial sensor 2682, a tri-axial sensor 2684, atri-axial sensor 2686, a tri-axial sensor 2688, and more as needed. Inmany examples, the tri-axial sensors 2680 can be positioned in themachine 2600 at locations that allow for the sensing of one of each ofthe bearing packs in the sets of bearings 2620 that is associated withthe rotating or oscillating components of the machine 2600. The machine2600 can also have temperature sensors 2700, such as a temperaturesensor 2702, a temperature sensor 2704, and more as needed. The machine2600 can also have a tachometer sensor 2710 or more as needed that eachdetail the RPMs of one of its rotating components.

By way of the above example, the second sensor ensemble 2650 can surveythe above sensors associated with the second machine 2600. To that end,the second ensemble 2650 can be configured to receive eight channels. Inother examples, the second sensor ensemble 2650 can be configured tohave more than eight channels or less than eight channels as needed. Inthis example, the eight channels include one channel that can monitor asingle-axis reference sensor signal and six channels that can monitortwo tri-axial sensor signals. The remaining channel can monitor atemperature signal. In one example, the second ensemble 2650 can monitorthe single axis sensor 2662, the tri-axial sensor 2682, the tri-axialsensor 2684, and the temperature sensor 2702. During a vibration surveyon the machine 2600 in accordance with the present disclosure, thesecond ensemble 2650 can first monitor the tri-axial sensor 2682simultaneously with the tri-axial sensor 2684 and then move onto thetri-axial sensor 2686 simultaneously with the tri-axial sensor 2688.

After monitoring the tri-axial sensors 2680, the second ensemble 2650can monitor additional tri-axial sensors (in simultaneous pairs) on themachine 2600 as needed and that are part of the predetermined route listassociated with the vibration survey of the machine 2600 in accordancewith the present disclosure. During this vibration survey, the secondensemble 2650 can continually monitor the single-axis sensor 2662 at itsunchanging location and the temperature sensor 2702 while the secondensemble 2650 can serially monitor the multiple tri-axial sensors in thepre-determined route plan for this vibration survey.

With continuing reference to FIG. 12, the many embodiments include athird machine 2800 having rotating or oscillating components 2810, orboth, each supported by a set of bearings 2820 including a bearing pack2822, a bearing pack 2824, a bearing pack 2826, and more as needed. Thethird machine 2800 can be monitored by a third sensor ensemble 2850. Thethird ensemble 2850 can be configured with a single-axis sensor 2860,and two tri-axial (e.g., orthogonal axes) sensors 2880, 2882. In manyexamples, the single axis-sensor 2860 can be secured by the user on themachine 2800 at a location that allows for the sensing of one of therotating or oscillating components of the machine 2800. The tri-axialsensors 2880, 2882 can be also be located on the machine 2800 by theuser at locations that allow for the sensing of one of each of thebearings in the sets of bearings that each associated with the rotatingor oscillating components of the machine 2800. The third ensemble 2850can also include a temperature sensor 2900. The third ensemble 2850 andits sensors can be moved to other machines unlike the first and secondensembles 2450, 2650.

The many embodiments also include a fourth machine 2950 having rotatingor oscillating components 2960, or both, each supported by a set ofbearings 2970 including a bearing pack 2972, a bearing pack 2974, abearing pack 2976, and more as needed. The fourth machine 2950 can bealso monitored by the third sensor ensemble 2850 when the user moves itto the fourth machine 2950. The many embodiments also include a fifthmachine 3000 having rotating or oscillating components 3010, or both.The fifth machine 3000 may not be explicitly monitored by any sensor orany sensor ensembles in operation but it can create vibrations or otherimpulse energy of sufficient magnitude to be recorded in the dataassociated with any one the machines 2400, 2600, 2800, 2950 under avibration survey.

The many embodiments include monitoring the first sensor ensemble 2450on the first machine 2400 through the predetermined route as disclosedherein. The many embodiments also include monitoring the second sensorensemble 2650 on the second machine 2600 through the predeterminedroute. The locations of machine 2400 being close to machine 2600 can beincluded in the contextual metadata of both vibration surveys. The thirdensemble 2850 can be moved between machine 2800, machine 2950, and othersuitable machines. The machine 3000 has no sensors onboard asconfigured, but could be monitored as needed by the third sensorensemble 2850. The machine 3000 and its operational characteristics canbe recorded in the metadata in relation to the vibration surveys on theother machines to note its contribution due to its proximity.

The many embodiments include hybrid database adaptation for harmonizingrelational metadata and streaming raw data formats. Unlike older systemsthat utilized traditional database structure for associating nameplateand operational parameters (sometimes deemed metadata) with individualdata measurements that are discrete and relatively simple, it will beappreciated in light of the disclosure that more modern systems cancollect relatively larger quantities of raw streaming data with highersampling rates and greater resolutions. At the same time, it will alsobe appreciated in light of the disclosure that the network of metadatawith which to link and obtain this raw data or correlate with this rawdata, or both, is expanding at ever-increasing rates.

In one example, a single overall vibration level can be collected aspart of a route or prescribed list of measurement points. This datacollected can then be associated with database measurement locationinformation for a point located on a surface of a bearing housing on aspecific piece of the machine adjacent to a coupling in a verticaldirection. Machinery analysis parameters relevant to the proper analysiscan be associated with the point located on the surface. Examples ofmachinery analysis parameters relevant to the proper analysis caninclude a running speed of a shaft passing through the measurement pointon the surface. Further examples of machinery analysis parametersrelevant to the proper analysis can include one of, or a combination of:running speeds of all component shafts for that piece of equipmentand/or machine, bearing types being analyzed such as sleeve or rollingelement bearings, the number of gear teeth on gears should there be agearbox, the number of poles in a motor, slip and line frequency of amotor, roller bearing element dimensions, number of fan blades, or thelike. Examples of machinery analysis parameters relevant to the properanalysis can further include machine operating conditions such as theload on the machines and whether load is expressed in percentage,wattage, air flow, head pressure, horsepower, and the like. Furtherexamples of machinery analysis parameters include information relevantto adjacent machines that might influence the data obtained during thevibration study.

It will be appreciated in light of the disclosure that the vast array ofequipment and machinery types can support many differentclassifications, each of which can be analyzed in distinctly differentways. For example, some machines, like screw compressors and hammermills, can be shown to run much noisier and can be expected to vibratesignificantly more than other machines. Machines known to vibrate moresignificantly can be shown to require a change in vibration levels thatcan be considered acceptable relative to quieter machines.

The present disclosure further includes hierarchical relationships foundin the vibrational data collected that can be used to support properanalysis of the data. One example of the hierarchical data includes theinterconnection of mechanical componentry such as a bearing beingmeasured in a vibration survey and the relationship between thatbearing, including how that bearing connects to a particular shaft onwhich is mounted a specific pinion within a particular gearbox, and therelationship between the shaft, the pinion, and the gearbox. Thehierarchical data can further include in what particular spot within amachinery gear train that the bearing being monitored is locatedrelative to other components in the machine. The hierarchical data canalso detail whether the bearing being measured in a machine is in closeproximity to another machine whose vibrations may affect what is beingmeasured in the machine that is the subject of the vibration study.

The analysis of the vibration data from the bearing or other componentsrelated to one another in the hierarchical data can use table lookups,searches for correlations between frequency patterns derived from theraw data, and specific frequencies from the metadata of the machine. Insome embodiments, the above can be stored in and retrieved from arelational database. In embodiments, National Instrument's TechnicalData Management Solution (TDMS) file format can be used. The TDMS fileformat can be optimized for streaming various types of measurement data(i.e., binary digital samples of waveforms), as well as also being ableto handle hierarchical metadata.

The many embodiments include a hybrid relational metadata-binary storageapproach (HRM-BSA). The HRM-BSA can include a structured query language(SQL) based relational database engine. The structured query languagebased relational database engine can also include a raw data engine thatcan be optimized for throughput and storage density for data that isflat and relatively structureless. It will be appreciated in light ofthe disclosure that benefits can be shown in the cooperation between thehierarchical metadata and the SQL relational database engine. In oneexample, marker technologies and pointer sign-posts can be used to makecorrelations between the raw database engine and the SQL relationaldatabase engine. Three examples of correlations between the raw databaseengine and the SQL relational database engine linkages include: (1)pointers from the SQL database to the raw data; (2) pointers from theancillary metadata tables or similar grouping of the raw data to the SQLdatabase; and (3) independent storage tables outside the domain ofeither the SQL data base or raw data technologies.

With reference to FIG. 13, the present disclosure can include pointersfor Group 1 and Group 2 that can include associated filenames, pathinformation, table names, database key fields as employed with existingSQL database technologies that can be used to associate a specificdatabase segments or locations, asset properties to specific measurementraw data streams, records with associated time/date stamps, orassociated metadata such as operating parameters, panel conditions andthe like. By way of this example, a plant 3200 can include machine one3202, machine two 3204, and many others in the plant 3200. The machineone 3202 can include a gearbox 3210, a motor 3212, and other elements.The machine two 3204 can include a motor 3220, and other elements. Manywaveforms 3230 including waveform 3240, waveform 3242, waveform 3244,and additional waveforms as needed can be acquired from the machines3202, 3204 in the plant 3200. The waveforms 3230 can be associated withthe local marker linking tables 3300 and the linking raw data tables3400. The machines 3202, 3204 and their elements can be associated withlinking tables having relational databases 3500. The linking tables rawdata tables 3400 and the linking tables having relational databases 3500can be associated with the linking tables with optional independentstorage tables 3600.

The present disclosure can include markers that can be applied to a timemark or a sample length within the raw waveform data. The markersgenerally fall into two categories: preset or dynamic. The presetmarkers can correlate to preset or existing operating conditions (e.g.,load, head pressure, air flow cubic feet per minute, ambienttemperature, RPMs, and the like.). These preset markers can be fed intothe data acquisition system directly. In certain instances, the presetmarkers can be collected on data channels in parallel with the waveformdata (e.g., waveforms for vibration, current, voltage, etc.).Alternatively, the values for the preset markers can be enteredmanually.

For dynamic markers such as trending data, it can be important tocompare similar data like comparing vibration amplitudes and patternswith a repeatable set of operating parameters. One example of thepresent disclosure includes one of the parallel channel inputs being akey phasor trigger pulse from an operating shaft that can provide RPMinformation at the instantaneous time of collection. In this example ofdynamic markers, sections of collected waveform data can be marked withappropriate speeds or speed ranges.

The present disclosure can also include dynamic markers that cancorrelate to data that can be derived from post processing and analyticsperformed on the sample waveform. In further embodiments, the dynamicmarkers can also correlate to post-collection derived parametersincluding RPMs, as well as other operationally derived metrics such asalarm conditions like a maximum RPM. In certain examples, many modernpieces of equipment that are candidates for a vibration survey with theportable data collection systems described herein do not includetachometer information. This can be true because it is not alwayspractical or cost-justifiable to add a tachometer even though themeasurement of RPM can be of primary importance for the vibration surveyand analysis. It will be appreciated that for fixed speed machineryobtaining an accurate RPM measurement can be less important especiallywhen the approximate speed of the machine can be ascertainedbefore-hand; however, variable-speed drives are becoming more and moreprevalent. It will also be appreciated in light of the disclosure thatvarious signal processing techniques can permit the derivation of RPMfrom the raw data without the need for a dedicated tachometer signal.

In many embodiments, the RPM information can be used to mark segments ofthe raw waveform data over its collection history. Further embodimentsinclude techniques for collecting instrument data following a prescribedroute of a vibration study. The dynamic markers can enable analysis andtrending software to utilize multiple segments of the collectioninterval indicated by the markers (e.g., two minutes) as multiplehistorical collection ensembles, rather than just one as done inprevious systems where route collection systems would historically storedata for only one RPM setting. This could, in turn, be extended to anyother operational parameter such as load setting, ambient temperature,and the like, as previously described. The dynamic markers, however,that can be placed in a type of index file pointing to the raw datastream can classify portions of the stream in homogenous entities thatcan be more readily compared to previously collected portions of the rawdata stream

The many embodiments include the hybrid relational metadata-binarystorage approach that can use the best of pre-existing technologies forboth relational and raw data streams. In embodiments, the hybridrelational metadata-binary storage approach can marry them together witha variety of marker linkages. The marker linkages can permit rapidsearches through the relational metadata and can allow for moreefficient analyses of the raw data using conventional SQL techniqueswith pre-existing technology. This can be shown to permit utilization ofmany of the capabilities, linkages, compatibilities, and extensions thatconventional database technologies do not provide.

The marker linkages can also permit rapid and efficient storage of theraw data using conventional binary storage and data compressiontechniques. This can be shown to permit utilization of many of thecapabilities, linkages, compatibilities, and extensions thatconventional raw data technologies provide such as TMDS (NationalInstruments), UFF (Universal File Format such as UFF58), and the like.The marker linkages can further permit using the marker technology linkswhere a vastly richer set of data from the ensembles can be amassed inthe same collection time as more conventional systems. The richer set ofdata from the ensembles can store data snapshots associated withpredetermined collection criterion and the proposed system can derivemultiple snapshots from the collected data streams utilizing the markertechnology. In doing so, it can be shown that a relatively richeranalysis of the collected data can be achieved. One such benefit caninclude more trending points of vibration at a specific frequency ororder of running speed versus RPM, load, operating temperature, flowrates and the like, which can be collected for a similar time relativeto what is spent collecting data with a conventional system.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals frommachines, elements of the machines and the environment of the machinesincluding heavy duty machines deployed at a local job site or atdistributed job sites under common control. The heavy-duty machines mayinclude earthmoving equipment, heavy duty on-road industrial vehicles,heavy duty off-road industrial vehicles, industrial machines deployed invarious settings such as turbines, turbomachinery, generators, pumps,pulley systems, manifold and valve systems, and the like. Inembodiments, heavy industrial machinery may also include earth-movingequipment, earth-compacting equipment, hauling equipment, hoistingequipment, conveying equipment, aggregate production equipment,equipment used in concrete construction, and piledriving equipment. Inexamples, earth moving equipment may include excavators, backhoes,loaders, bulldozers, skid steer loaders, trenchers, motor graders, motorscrapers, crawker loaders, and wheeled loading shovels. In examples,construction vehicles may include dumpers, tankers, tippers, andtrailers. In examples, material handling equipment may include cranes,conveyors, forklift, and hoists. In examples, construction equipment mayinclude tunnel and handling equipment, road rollers, concrete mixers,hot mix plants, road making machines (compactors), stone crashers,pavers, slurry seal machines, spraying and plastering machines, andheavy-duty pumps. Further examples of heavy industrial equipment mayinclude different systems such as implement traction, structure, powertrain, control, and information. Heavy industrial equipment may includemany different powertrains and combinations thereof to provide power forlocomotion and to also provide power to accessories and onboardfunctionality. In each of these examples, the platform 100 may deploythe local data collection system 102 into the environment 104 in whichthese machines, motors, pumps, and the like, operate and directlyconnected integrated into each of the machines, motors, pumps, and thelike.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals frommachines in operation and machines in being constructed such as turbineand generator sets like Siemens™ SGT6-5000F™ gas turbine, an SST-900™steam turbine, an SGen6-1000A™ generator, and an SGen6-100A™ generator,and the like. In embodiments, the local data collection system 102 maybe deployed to monitor steam turbines as they rotate in the currentscaused by hot water vapor that may be directed through the turbine butotherwise generated from a different source such as from gas-firedburners, nuclear cores, molten salt loops and the like. In thesesystems, the local data collection system 102 may monitor the turbinesand the water or other fluids in a closed loop cycle in which watercondenses and is then heated until it evaporates again. The local datacollection system 102 may monitor the steam turbines separately from thefuel source deployed to heat the water to steam. In examples, workingtemperatures of steam turbines may be between 500 and 650° C. In manyembodiments, an array of steam turbines may be arranged and configuredfor high, medium, and low pressure, so they may optimally convert therespective steam pressure into rotational movement.

The local data collection system 102 may also be deployed in a gasturbines arrangement and therefore not only monitor the turbine inoperation but also monitor the hot combustion gases feed into theturbine that may be in excess of 1,500° C. Because these gases are muchhotter than those in steam turbines, the blades may be cooled with airthat may flow out of small openings to create a protective film orboundary layer between the exhaust gases and the blades. Thistemperature profile may be monitored by the local data collection system102. Gas turbine engines, unlike typical steam turbines, include acompressor, a combustion chamber, and a turbine all of which arejournaled for rotation with a rotating shaft. The construction andoperation of each of these components may be monitored by the local datacollection system 102.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals from waterturbines serving as rotary engines that may harvest energy from movingwater and are used for electric power generation. The type of waterturbine or hydro-power selected for a project may be based on the heightof standing water, often referred to as head, and the flow, or volume ofwater, at the site. In this example, a generator may be placed at thetop of a shaft that connects to the water turbine. As the turbinecatches the naturally moving water in its blade and rotates, the turbinesends rotational power to the generator to generate electrical energy.In doing so, the platform 100 may monitor signals from the generators,the turbines, the local water system, flow controls such as dam windowsand sluices. Moreover, the platform 100 may monitor local conditions onthe electric grid including load, predicted demand, frequency response,and the like, and include such information in the monitoring and controldeployed by platform 100 in these hydroelectric settings.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals fromenergy production environments, such as thermal, nuclear, geothermal,chemical, biomass, carbon-based fuels, hybrid-renewable energy plants,and the like. Many of these plants may use multiple forms of energyharvesting equipment like wind turbines, hydro turbines, and steamturbines powered by heat from nuclear, gas-fired, solar, and molten saltheat sources. In embodiments, elements in such systems may includetransmission lines, heat exchangers, desulphurization scrubbers, pumps,coolers, recuperators, chillers, and the like. In embodiments, certainimplementations of turbomachinery, turbines, scroll compressors, and thelike may be configured in arrayed control so as to monitor largefacilities creating electricity for consumption, providingrefrigeration, creating steam for local manufacture and heating, and thelike, and that arrayed control platforms may be provided by the providerof the industrial equipment such as Honeywell and their Experion™ PKSplatform. In embodiments, the platform 100 may specifically communicatewith and integrate the local manufacturer-specific controls and mayallow equipment from one manufacturer to communicate with otherequipment. Moreover, the platform 100 provides allows for the local datacollection system 102 to collect information across systems from manydifferent manufacturers. In embodiments, the platform 100 may includethe local data collection system 102 deployed in the environment 104 tomonitor signals from marine industrial equipment, marine diesel engines,shipbuilding, oil and gas plants, refineries, petrochemical plant,ballast water treatment solutions, marine pumps and turbines and thelike.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals from heavyindustrial equipment and processes including monitoring one or moresensors. By way of this example, sensors may be devices that may be usedto detect or respond to some type of input from a physical environment,such as an electrical, heat, or optical signal. In embodiments, thelocal data collection system 102 may include multiple sensors such as,without limitation, a temperature sensor, a pressure sensor, a torquesensor, a flow sensor, a heat sensors, a smoke sensor, an arc sensor, aradiation sensor, a position sensor, an acceleration sensor, a strainsensor, a pressure cycle sensor, a pressure sensor, an air temperaturesensor, and the like. The torque sensor may encompass a magnetic twistangle sensor. In one example, the torque and speed sensors in the localdata collection system 102 may be similar to those discussed in U.S.Pat. No. 8,352,149 to Meachem, issued 8 Jan. 2013 and herebyincorporated by reference as if fully set forth herein. In embodiments,one or more sensors may be provided such as a tactile sensor, abiosensor, a chemical sensor, an image sensor, a humidity sensor, aninertial sensor, and the like.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals fromsensors that may provide signals for fault detection including excessivevibration, incorrect material, incorrect material properties, truenessto the proper size, trueness to the proper shape, proper weight,trueness to balance. Additional fault sensors include those forinventory control and for inspections such as to confirming that partspackaged to plan, parts are to tolerance in a plan, occurrence ofpackaging damage or stress, and sensors that may indicate the occurrenceof shock or damage in transit. Additional fault sensors may includedetection of the lack of lubrication, over lubrication, the need forcleaning of the sensor detection window, the need for maintenance due tolow lubrication, the need for maintenance due to blocking or reducedflow in a lubrication region, and the like.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 that includes aircraftoperations and manufacture including monitoring signals from sensors forspecialized applications such as sensors used in an aircraft's Attitudeand Heading Reference System (AHRS), such as gyroscopes, accelerometers,and magnetometers. In embodiments, the platform 100 may include thelocal data collection system 102 deployed in the environment 104 tomonitor signals from image sensors such as semiconductor charge coupleddevices (CCDs), active pixel sensors, in complementarymetal-oxide-semiconductor (CMOS) or N-type metal-oxide-semiconductor(NMOS, Live MOS) technologies. In embodiments, the platform 100 mayinclude the local data collection system 102 deployed in the environment104 to monitor signals from sensors such as an infra-red (IR) sensor, anultraviolet (UV) sensor, a touch sensor, a proximity sensor, and thelike. In embodiments, the platform 100 may include the local datacollection system 102 deployed in the environment 104 to monitor signalsfrom sensors configured for optical character recognition (OCR), readingbarcodes, detecting surface acoustic waves, detecting transponders,communicating with home automation systems, medical diagnostics, healthmonitoring, and the like.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals fromsensors such as a Micro-Electro-Mechanical Systems (MEMS) sensor, suchas ST Microelectronic's™ LSM303AH smart MEMS sensor, which may includean ultra-low-power high-performance system-in-package featuring a 3Ddigital linear acceleration sensor and a 3D digital magnetic sensor.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals fromadditional large machines such as turbines, windmills, industrialvehicles, robots, and the like. These large mechanical machines includemultiple components and elements providing multiple subsystems on eachmachine. Toward that end, the platform 100 may include the local datacollection system 102 deployed in the environment 104 to monitor signalsfrom individual elements such as axles, bearings, belts, buckets, gears,shafts, gear boxes, cams, carriages, camshafts, clutches, brakes, drums,dynamos, feeds, flywheels, gaskets, pumps, jaws, robotic arms, seals,sockets, sleeves, valves, wheels, actuators, motors, servomotor, and thelike. Many of the machines and their elements may include servomotors.The local data collection system 102 may monitor the motor, the rotaryencoder, and the potentiometer of the servomechanism to providethree-dimensional detail of position, placement, and progress ofindustrial processes.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals from geardrives, powertrains, transfer cases, multispeed axles, transmissions,direct drives, chain drives, belt-drives, shaft-drives, magnetic drives,and similar meshing mechanical drives. In embodiments, the platform 100may include the local data collection system 102 deployed in theenvironment 104 to monitor signals from fault conditions of industrialmachines that may include overheating, noise, grinding gears, lockedgears, excessive vibration, wobbling, under-inflation, over-inflation,and the like. Operation faults, maintenance indicators, and interactionsfrom other machines may cause maintenance or operational issues mayoccur during operation, during installation, and during maintenance. Thefaults may occur in the mechanisms of the industrial machines but mayalso occur in infrastructure that supports the machine such as itswiring and local installation platforms. In embodiments, the largeindustrial machines may face different types of fault conditions such asoverheating, noise, grinding gears, excessive vibration of machineparts, fan vibration problems, problems with large industrial machinesrotating parts.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals fromindustrial machinery including failures that may be caused by prematurebearing failure that may occur due to contamination or loss of bearinglubricant. In another example, a mechanical defect such as misalignmentof bearings may occur. Many factors may contribute to the failure suchas metal fatigue, therefore, the local data collection system 102 maymonitor cycles and local stresses. By way of this example, the platform100 may monitor incorrect operation of machine parts, lack ofmaintenance and servicing of parts, corrosion of vital machine parts,such as couplings or gearboxes, misalignment of machine parts, and thelike. Though the fault occurrences cannot be completely stopped, manyindustrial breakdowns may be mitigated to reduce operational andfinancial losses. The platform 100 provides real-time monitoring andpredictive maintenance in many industrial environments wherein it hasbeen shown to present a cost-savings over regularly-scheduledmaintenance processes that replace parts according to a rigid expirationof time and not actual load and wear and tear on the element or machine.To that end, the platform 10 may provide reminders of, or perform some,preventive measures such as adhering to operating manual and modeinstructions for machines, proper lubrication, and maintenance ofmachine parts, minimizing or eliminating overrun of machines beyondtheir defined capacities, replacement of worn but still functional partsas needed, properly training the personnel for machine use, and thelike.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor multiple signalsthat may be carried by a plurality of physical, electronic, and symbolicformats or signals. The platform 100 may employ signal processingincluding a plurality of mathematical, statistical, computational,heuristic, and linguistic representations and processing of signals anda plurality of operations needed for extraction of useful informationfrom signal processing operations such as techniques for representation,modeling, analysis, synthesis, sensing, acquisition, and extraction ofinformation from signals. In examples, signal processing may beperformed using a plurality of techniques, including but not limited totransformations, spectral estimations, statistical operations,probabilistic and stochastic operations, numerical theory analysis, datamining, and the like. The processing of various types of signals formsthe basis of many electrical or computational process. As a result,signal processing applies to almost all disciplines and applications inthe industrial environment such as audio and video processing, imageprocessing, wireless communications, process control, industrialautomation, financial systems, feature extraction, quality improvementssuch as noise reduction, image enhancement, and the like. Signalprocessing for images may include pattern recognition for manufacturinginspections, quality inspection, and automated operational inspectionand maintenance. The platform 100 may employ many pattern recognitiontechniques including those that may classify input data into classesbased on key features with the objective of recognizing patterns orregularities in data. The platform 100 may also implement patternrecognition processes with machine learning operations and may be usedin applications such as computer vision, speech and text processing,radar processing, handwriting recognition, CAD systems, and the like.The platform 100 may employ supervised classification and unsupervisedclassification. The supervised learning classification algorithms may bebased to create classifiers for image or pattern recognition, based ontraining data obtained from different object classes. The unsupervisedlearning classification algorithms may operate by finding hiddenstructures in unlabeled data using advanced analysis techniques such assegmentation and clustering. For example, some of the analysistechniques used in unsupervised learning may include K-means clustering,Gaussian mixture models, Hidden Markov models, and the like. Thealgorithms used in supervised and unsupervised learning methods ofpattern recognition enable the use of pattern recognition in varioushigh precision applications. The platform 100 may use patternrecognition in face detection related applications such as securitysystems, tracking, sports related applications, fingerprint analysis,medical and forensic applications, navigation and guidance systems,vehicle tracking, public infrastructure systems such as transportsystems, license plate monitoring, and the like.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 using machine learning toenable derivation-based learning outcomes from computers without theneed to program them. The platform 100 may, therefore, learn from andmake decisions on a set of data, by making data-driven predictions andadapting according to the set of data. In embodiments, machine learningmay involve performing a plurality of machine learning tasks by machinelearning systems, such as supervised learning, unsupervised learning,and reinforcement learning. Supervised learning may include presenting aset of example inputs and desired outputs to the machine learningsystems. Unsupervised learning may include the learning algorithm itselfstructuring its input by methods such as pattern detection and/orfeature learning. Reinforcement learning may include the machinelearning systems performing in a dynamic environment and then providingfeedback about correct and incorrect decisions. In examples, machinelearning may include a plurality of other tasks based on an output ofthe machine learning system. In examples, the tasks may also beclassified as machine learning problems such as classification,regression, clustering, density estimation, dimensionality reduction,anomaly detection, and the like. In examples, machine learning mayinclude a plurality of mathematical and statistical techniques. Inexamples, the many types of machine learning algorithms may includedecision tree based learning, association rule learning, deep learning,artificial neural networks, genetic learning algorithms, inductive logicprogramming, support vector machines (SVMs), Bayesian network,reinforcement learning, representation learning, rule-based machinelearning, sparse dictionary learning, similarity and metric learning,learning classifier systems (LCS), logistic regression, random forest,K-Means, gradient boost and adaboost, K-nearest neighbors (KNN), apriori algorithms, and the like. In embodiments, certain machinelearning algorithms may be used (such as genetic algorithms defined forsolving both constrained and unconstrained optimization problems thatmay be based on natural selection, the process that drives biologicalevolution). By way of this example, genetic algorithms may be deployedto solve a variety of optimization problems that are not well suited forstandard optimization algorithms, including problems in which theobjective functions are discontinuous, not differentiable, stochastic,or highly nonlinear. In an example, the genetic algorithm may be used toaddress problems of mixed integer programming, where some componentsrestricted to being integer-valued. Genetic algorithms and machinelearning techniques and systems may be used in computationalintelligence systems, computer vision, Natural Language Processing(NLP), recommender systems, reinforcement learning, building graphicalmodels, and the like. By way of this example, the machine learningsystems may be used to perform intelligent computing based control andbe responsive to tasks in a wide variety of systems (such as interactivewebsites and portals, brain-machine interfaces, online security andfraud detection systems, medical applications such as diagnosis andtherapy assistance systems, classification of DNA sequences, and thelike). In examples, machine learning systems may be used in advancedcomputing applications (such as online advertising, natural languageprocessing, robotics, search engines, software engineering, speech andhandwriting recognition, pattern matching, game playing, computationalanatomy, bioinformatics systems and the like). In an example, machinelearning may also be used in financial and marketing systems (such asfor user behavior analytics, online advertising, economic estimations,financial market analysis, and the like).

Additional details are provided below in connection with the methods,systems, devices, and components depicted in connection with FIGS. 1through 6. In embodiments, methods and systems are disclosed herein forcloud-based, machine pattern recognition based on fusion of remote,analog industrial sensors. For example, data streams from vibration,pressure, temperature, accelerometer, magnetic, electrical field, andother analog sensors may be multiplexed or otherwise fused, relayed overa network, and fed into a cloud-based machine learning facility, whichmay employ one or more models relating to an operating characteristic ofan industrial machine, an industrial process, or a component or elementthereof. A model may be created by a human who has experience with theindustrial environment and may be associated with a training data set(such as created by human analysis or machine analysis of data that iscollected by the sensors in the environment, or sensors in other similarenvironments. The learning machine may then operate on other data,initially using a set of rules or elements of a model, such as toprovide a variety of outputs, such as classification of data into types,recognition of certain patterns (such as ones indicating the presence offaults, or ones indicating operating conditions, such as fuelefficiency, energy production, or the like). The machine learningfacility may take feedback, such as one or more inputs or measures ofsuccess, such that it may train, or improve, its initial model (such asby adjusting weights, rules, parameters, or the like, based on thefeedback). For example, a model of fuel consumption by an industrialmachine may include physical model parameters that characterize weights,motion, resistance, momentum, inertia, acceleration, and other factorsthat indicate consumption, and chemical model parameters (such as onesthat predict energy produced and/or consumed e.g., such as throughcombustion, through chemical reactions in battery charging anddischarging, and the like). The model may be refined by feeding in datafrom sensors disposed in the environment of a machine, in the machine,and the like, as well as data indicating actual fuel consumption, sothat the machine can provide increasingly accurate, sensor-based,estimates of fuel consumption and can also provide output that indicatewhat changes can be made to increase fuel consumption (such as changingoperation parameters of the machine or changing other elements of theenvironment, such as the ambient temperature, the operation of a nearbymachine, or the like). For example, if a resonance effect between twomachines is adversely affecting one of them, the model may account forthis and automatically provide an output that results in changing theoperation of one of the machines (such as to reduce the resonance, toincrease fuel efficiency of one or both machines). By continuouslyadjusting parameters to cause outputs to match actual conditions, themachine learning facility may self-organize to provide a highly accuratemodel of the conditions of an environment (such as for predictingfaults, optimizing operational parameters, and the like). This may beused to increase fuel efficiency, to reduce wear, to increase output, toincrease operating life, to avoid fault conditions, and for many otherpurposes.

FIG. 14 illustrates components and interactions of a data collectionarchitecture involving application of cognitive and machine learningsystems to data collection and processing. Referring to FIG. 14, a datacollection system 102 may be disposed in an environment (such as anindustrial environment where one or more complex systems, such aselectro-mechanical systems and machines are manufactured, assembled, oroperated). The data collection system 102 may include onboard sensorsand may take input, such as through one or more input interfaces orports 4008, from one or more sensors (such as analog or digital sensorsof any type disclosed herein) and from one or more input sources 116(such as sources that may be available through Wi-Fi, Bluetooth, NFC, orother local network connections or over the Internet). Sensors may becombined and multiplexed (such as with one or more multiplexers 4002).Data may be cached or buffered in a cache/buffer 4022 and made availableto external systems, such as a remote host processing system 112 asdescribed elsewhere in this disclosure (which may include an extensiveprocessing architecture 4024, including any of the elements described inconnection with other embodiments described throughout this disclosureand in the Figure), though one or more output interfaces and ports 4010(which may in embodiments be separate from or the same as the inputinterfaces and ports 4008). The data collection system 102 may beconfigured to take input from a host processing system 112, such asinput from an analytic system 4018, which may operate on data from thedata collection system 102 and data from other input sources 116 toprovide analytic results, which in turn may be provided as a learningfeedback input 4012 to the data collection system, such as to assist inconfiguration and operation of the data collection system 102.

Combination of inputs (including selection of what sensors or inputsources to turn “on” or “off”) may be performed under the control ofmachine-based intelligence, such as using a local cognitive inputselection system 4004, an optionally remote cognitive input selectionsystem 4114, or a combination of the two. The cognitive input selectionsystems 4004, 4014 may use intelligence and machine learningcapabilities described elsewhere in this disclosure, such as usingdetected conditions (such as informed by the input sources 116 orsensors), state information (including state information determined by amachine state recognition system 4020 that may determine a state), suchas relating to an operational state, an environmental state, a statewithin a known process or workflow, a state involving a fault ordiagnostic condition, or many others. This may include optimization ofinput selection and configuration based on learning feedback from thelearning feedback system 4012, which may include providing training data(such as from the host processing system 112 or from other datacollection systems 102 either directly or from the host 112) and mayinclude providing feedback metrics, such as success metrics calculatedwithin the analytic system 4018 of the host processing system 112. Forexample, if a data stream consisting of a particular combination ofsensors and inputs yields positive results in a given set of conditions(such as providing improved pattern recognition, improved prediction,improved diagnosis, improved yield, improved return on investment,improved efficiency, or the like), then metrics relating to such resultsfrom the analytic system 4018 can be provided via the learning feedbacksystem 4012 to the cognitive input selection systems 4004, 4014 to helpconfigure future data collection to select that combination in thoseconditions (allowing other input sources to be de-selected, such as bypowering down the other sensors). In embodiments, selection andde-selection of sensor combinations, under control of one or more of thecognitive input selection systems 4004, may occur with automatedvariation, such as using genetic programming techniques, such that overtime, based on learning feedback 4012, such as from the analytic system4018, effective combinations for a given state or set of conditions arepromoted, and less effective combinations are demoted, resulting inprogressive optimization and adaptation of the local data collectionsystem to each unique environment. Thus, an automatically adapting,multi-sensor data collection system is provided, where cognitive inputselection is used, with feedback, to improve the effectiveness,efficiency, or other performance parameter of the data collection systemwithin its particular environment. Performance parameters may relate tooverall system metrics (such as financial yields, process optimizationresults, energy production or usage, and the like), analytic metrics(such as success in recognizing patterns, making predictions,classifying data, or the like), and local system metrics (such asbandwidth utilization, storage utilization, power consumption, and thelike). In embodiments, the analytic system 4018, the state system 4020and the cognitive input selection system 4114 of a host may take datafrom multiple data collection systems 102, such that optimization(including of input selection) may be undertaken through coordinatedoperation of multiple systems 102. For example, the cognitive inputselection system 4114 may understand that if one data collection system102 is already collecting vibration data for an X-axis, the X-axisvibration sensor for the other data collection system might be turnedoff, in favor of getting Y-axis data from the other data collector 102.Thus, through coordinated collection by the host cognitive inputselection system 4114, the activity of multiple collectors 102, across ahost of different sensors, can provide for a rich data set for the hostprocessing system 112, without wasting energy, bandwidth, storage space,or the like. As noted above, optimization may be based on overall systemsuccess metrics, analytic success metrics, and local system metrics, ora combination of the above.

Methods and systems are disclosed herein for cloud-based, machinepattern analysis of state information from multiple industrial sensorsto provide anticipated state information for an industrial system. Inembodiments, machine learning may take advantage of a state machine,such as tracking states of multiple analog and/or digital sensors,feeding the states into a pattern analysis facility, and determininganticipated states of the industrial system based on historical dataabout sequences of state information. For example, where a temperaturestate of an industrial machine exceeds a certain threshold and isfollowed by a fault condition, such as breaking down of a set ofbearings, that temperature state may be tracked by a pattern recognizer,which may produce an output data structure indicating an anticipatedbearing fault state (whenever an input state of a high temperature isrecognized). A wide range of measurement values and anticipated statesmay be managed by a state machine, relating to temperature, pressure,vibration, acceleration, momentum, inertia, friction, heat, heat flux,galvanic states, magnetic field states, electrical field states,capacitance states, charge and discharge states, motion, position, andmany others. States may comprise combined states, where a data structureincludes a series of states, each of which is represented by a place ina byte-like data structure. For example, an industrial machine may becharacterized by a genetic structure, such as one that providespressure, temperature, vibration, and acoustic data, the measurement ofwhich takes one place in the data structure, so that the combined statecan be operated on as a byte-like structure, such as for compactlycharacterizing the current combined state of the machine or environment,or compactly characterizing the anticipated state. This byte-likestructure can be used by a state machine for machine learning, such asby pattern recognition that operates on the structure to determinepatterns that reflect combined effects of multiple conditions. A widevariety of such structure can be tracked and used, such as in machinelearning, representing various combinations, of various length, of thedifferent elements that can be sensed in an industrial environment. Inembodiments, byte-like structures can be used in a genetic programmingtechnique, such as by substituting different types of data, or data fromvarying sources, and tracking outcomes over time, so that one or morefavorable structures emerges based on the success of those structureswhen used in real world situations, such as indicating successfulpredictions of anticipated states, or achievement of success operationaloutcomes, such as increased efficiency, successful routing ofinformation, achieving increased profits, or the like. That is, byvarying what data types and sources are used in byte-like structuresthat are used for machine optimization over time, a geneticprogramming-based machine learning facility can “evolve” a set of datastructures, consisting of a favorable mix of data types (e.g., pressure,temperature, and vibration), from a favorable mix of data sources (e.g.,temperature is derived from sensor X, while vibration comes from sensorY), for a given purpose. Different desired outcomes may result indifferent data structures that are best adapted to support effectiveachievement of those outcomes over time with application of machinelearning and promotion of structures with favorable results for thedesired outcome in question by genetic programming. The promoted datastructures may provide compact, efficient data for various activities asdescribed throughout this disclosure, including being stored in datapools (which may be optimized by storing favorable data structures thatprovide the best operational results for a given environment), beingpresented in data marketplaces (such as being presented as the mosteffective structures for a given purpose), and the like.

In embodiments, a platform is provided having cloud-based, machinepattern analysis of state information from multiple analog industrialsensors to provide anticipated state information for an industrialsystem. In embodiments, the host processing system 112, such as disposedin the cloud, may include the state system 4020, which may be used toinfer or calculate a current state or to determine an anticipated futurestate relating to the data collection system 102 or some aspect of theenvironment in which the data collection system 102 is disposed, such asthe state of a machine, a component, a workflow, a process, an event(e.g., whether the event has occurred), an object, a person, acondition, a function, or the like. Maintaining state information allowsthe host processing system 112 to undertake analysis, such as in one ormore analytic systems 4018, to determine contextual information, toapply semantic and conditional logic, and perform many other functionsas enabled by the processing architecture 4024 described throughout thisdisclosure.

In embodiments, a platform is provided having cloud-based policyautomation engine for IoT, with creation, deployment, and management ofIoT devices. In embodiments, the platform 100 includes (or is integratedwith, or included in) the host processing system 112, such as on a cloudplatform, a policy automation engine 4032 for automating creation,deployment, and management of policies to IoT devices. Polices, whichmay include access policies, network usage policies, storage usagepolicies, bandwidth usage policies, device connection policies, securitypolicies, rule-based policies, role-based polices, and others, may berequired to govern the use of IoT devices. For example, as IoT devicesmay have many different network and data communications to otherdevices, policies may be needed to indicate to what devices a givendevice can connect, what data can be passed on, and what data can bereceived. As billions of devices with countless potential connectionsare expected to be deployed in the near future, it becomes impossiblefor humans to configure policies for IoT devices on aconnection-by-connection basis. Accordingly, an intelligent policyautomation engine 4032 may include cognitive features for creating,configuring, and managing policies. The policy automation engine 4032may consume information about possible policies, such as from a policydatabase or library, which may include one or more public sources ofavailable policies. These may be written in one or more conventionalpolicy languages or scripts. The policy automation engine 4032 may applythe policies according to one or more models, such as based on thecharacteristics of a given device, machine, or environment. For example,a large machine, such as for power generation, may include a policy thatonly a verifiably local controller can change certain parameters of thepower generation, thereby avoiding a remote “takeover” by a hacker. Thismay be accomplished in turn by automatically finding and applyingsecurity policies that bar connection of the control infrastructure ofthe machine to the Internet, by requiring access authentication, or thelike. The policy automation engine 4032 may include cognitive features,such as varying the application of policies, the configuration ofpolicies, and the like (such as based on state information from thestate system 4020). The policy automation engine 4032 may take feedback,as from the learning feedback system 4012, such as based on one or moreanalytic results from the analytic system 4018, such as based on overallsystem results (such as the extent of security breaches, policyviolations, and the like), local results, and analytic results. Byvariation and selection based on such feedback, the policy automationengine 4032 can, over time, learn to automatically create, deploy,configure, and manage policies across very large numbers of devices,such as managing policies for configuration of connections among IoTdevices.

Methods and systems are disclosed herein for on-device sensor fusion anddata storage for industrial IoT devices, including on-device sensorfusion and data storage for an industrial IoT device, where data frommultiple sensors is multiplexed at the device for storage of a fuseddata stream. For example, pressure and temperature data may bemultiplexed into a data stream that combines pressure and temperature ina time series, such as in a byte-like structure (where time, pressure,and temperature are bytes in a data structure, so that pressure andtemperature remain linked in time, without requiring separate processingof the streams by outside systems), or by adding, dividing, multiplying,subtracting, or the like, such that the fused data can be stored on thedevice. Any of the sensor data types described throughout thisdisclosure can be fused in this manner and stored in a local data pool,in storage, or on an IoT device, such as a data collector, a componentof a machine, or the like.

In embodiments, a platform is provided having on-device sensor fusionand data storage for industrial IoT devices. In embodiments, a cognitivesystem is used for a self-organizing storage system 4028 for the datacollection system 102. Sensor data, and in particular analog sensordata, can consume large amounts of storage capacity, in particular wherea data collector 102 has multiple sensor inputs onboard or from thelocal environment. Simply storing all the data indefinitely is nottypically a favorable option, and even transmitting all of the data maystrain bandwidth limitations, exceed bandwidth permissions (such asexceeding cellular data plan capacity), or the like. Accordingly,storage strategies are needed. These typically include capturing onlyportions of the data (such as snapshots), storing data for limited timeperiods, storing portions of the data (such as intermediate orabstracted forms), and the like. With many possible selections amongthese and other options, determining the correct storage strategy may behighly complex. In embodiments, the self-organizing storage system 4028may use a cognitive system, based on learning feedback 4012, and usevarious metrics from the analytic system 4018 or other system of thehost cognitive input selection system 4114, such as overall systemmetrics, analytic metrics, and local performance indicators. Theself-organizing storage system 4028 may automatically vary storageparameters, such as storage locations (including local storage on thedata collection system 102, storage on nearby data collection systems102 (such as using peer-to-peer organization) and remote storage, suchas network-based storage), storage amounts, storage duration, type ofdata stored (including individual sensors or input sources 116, as wellas various combined or multiplexed data, such as selected under thecognitive input selection systems 4004, 4014), storage type (such asusing RAM, Flash, or other short-term memory versus available hard drivespace), storage organization (such as in raw form, in hierarchies, andthe like), and others. Variation of the parameters may be undertakenwith feedback, so that over time the data collection system 102 adaptsits storage of data to optimize itself to the conditions of itsenvironment, such as a particular industrial environment, in a way thatresults in its storing the data that is needed in the right amounts andof the right type for availability to users.

In embodiments, the local cognitive input selection system 4004 mayorganize fusion of data for various onboard sensors, external sensors(such as in the local environment) and other input sources 116 to thelocal collection system 102 into one or more fused data streams, such asusing the multiplexer 4002 to create various signals that representcombinations, permutations, mixes, layers, abstractions, data-metadatacombinations, and the like of the source analog and/or digital data thatis handled by the data collection system 102. The selection of aparticular fusion of sensors may be determined locally by the cognitiveinput selection system 4004, such as based on learning feedback from thelearning feedback system 4012, such as various overall system, analyticsystem and local system results and metrics. In embodiments, the systemmay learn to fuse particular combinations and permutations of sensors,such as in order to best achieve correct anticipation of state, asindicated by feedback of the analytic system 4018 regarding its abilityto predict future states, such as the various states handled by thestate system 4020. For example, the input selection system 4004 mayindicate selection of a sub-set of sensors among a larger set ofavailable sensors, and the inputs from the selected sensors may becombined, such as by placing input from each of them into a byte of adefined, multi-bit data structure (such as by taking a signal from eachat a given sampling rate or time and placing the result into the bytestructure, then collecting and processing the bytes over time), bymultiplexing in the multiplexer 4002, such as by additive mixing ofcontinuous signals, and the like. Any of a wide range of signalprocessing and data processing techniques for combination and fusing maybe used, including convolutional techniques, coercion techniques,transformation techniques, and the like. The particular fusion inquestion may be adapted to a given situation by cognitive learning, suchas by having the cognitive input selection system 4004 learn, based onfeedback 4012 from results (such as conveyed by the analytic system4018), such that the local data collection system 102 executescontext-adaptive sensor fusion.

In embodiments, the analytic system 4018 may apply to any of a widerange of analytic techniques, including statistical and econometrictechniques (such as linear regression analysis, use similarity matrices,heat map based techniques, and the like), reasoning techniques (such asBayesian reasoning, rule-based reasoning, inductive reasoning, and thelike), iterative techniques (such as feedback, recursion, feed-forwardand other techniques), signal processing techniques (such as Fourier andother transforms), pattern recognition techniques (such as Kalman andother filtering techniques), search techniques, probabilistic techniques(such as random walks, random forest algorithms, and the like),simulation techniques (such as random walks, random forest algorithms,linear optimization and the like), and others. This may includecomputation of various statistics or measures. In embodiments, theanalytic system 4018 may be disposed, at least in part, on a datacollection system 102, such that a local analytic system can calculateone or more measures, such as relating to any of the items notedthroughout this disclosure. For example, measures of efficiency, powerutilization, storage utilization, redundancy, entropy, and other factorsmay be calculated onboard, so that the data collection 102 can enablevarious cognitive and learning functions noted throughout thisdisclosure without dependence on a remote (e.g., cloud-based) analyticsystem.

In embodiments, the host processing system 112, a data collection system102, or both, may include, connect to, or integrate with, aself-organizing networking system 4020, which may comprise a cognitivesystem for providing machine-based, intelligent or organization ofnetwork utilization for transport of data in a data collection system,such as for handling analog and other sensor data, or other source data,such as among one or more local data collection systems 102 and a hostsystem 112. This may include organizing network utilization for sourcedata delivered to data collection systems, for feedback data, such asanalytic data provided to or via a learning feedback system 4012, datafor supporting a marketplace (such as described in connection with otherembodiments), and output data provided via output interfaces and ports4010 from one or more data collection systems 102.

Methods and systems are disclosed herein for a self-organizing datamarketplace for industrial IoT data, including where available dataelements are organized in the marketplace for consumption by consumersbased on training a self-organizing facility with a training set andfeedback from measures of marketplace success. A marketplace may be setup initially to make available data collected from one or moreindustrial environments, such as presenting data by type, by source, byenvironment, by machine, by one or more patterns, or the like (such asin a menu or hierarchy). The marketplace may vary the data collected,the organization of the data, the presentation of the data (includingpushing the data to external sites, providing links, configuring APIs bywhich the data may be accessed, and the like), the pricing of the data,or the like, such as under machine learning, which may vary differentparameters of any of the foregoing. The machine learning facility maymanage all of these parameters by self-organization, such as by varyingparameters over time (including by varying elements of the data typespresented, the data sourced used to obtain each type of data, the datastructures presented (such as byte-like structures, fused or multiplexedstructures (such as representing multiple sensor types), and statisticalstructures (such as representing various mathematical products of sensorinformation), among others), the pricing for the data, where the data ispresented, how the data is presented (such as by APIs, by links, by pushmessaging, and the like), how the data is stored, how the data isobtained, and the like. As parameters are varied, feedback may beobtained as to measures of success, such as number of views, yield(e.g., price paid) per access, total yield, per unit profit, aggregateprofit, and many others, and the self-organizing machine learningfacility may promote configurations that improve measures of success anddemote configurations that do not, so that, over time, the marketplaceis progressively configured to present favorable combinations of datatypes (e.g., ones that provide robust prediction of anticipated statesof particular industrial environments of a given type), from favorablesources (e.g., ones that are reliable, accurate and low priced), witheffective pricing (e.g., pricing that tends to provide high aggregateprofit from the marketplace). The marketplace may include spiders, webcrawlers, and the like to seek input data sources, such as finding datapools, connected IoT devices, and the like that publish potentiallyrelevant data. These may be trained by human users and improved bymachine learning in a manner similar to that described elsewhere in thisdisclosure.

In embodiments, a platform is provided having a self-organizing datamarketplace for industrial IoT data. Referring to FIG. 15, inembodiments, a platform is provided having a cognitive data marketplace4102, referred to in some cases as a self-organizing data marketplace,for data collected by one or more data collection systems 102 or fordata from other sensors or input sources 116 that are located in variousdata collection environments, such as industrial environments. Inaddition to data collection systems 102, this may include datacollected, handled or exchanged by IoT devices, such as cameras,monitors, embedded sensors, mobile devices, diagnostic devices andsystems, instrumentation systems, telematics systems, and the like, suchas for monitoring various parameters and features of machines, devices,components, parts, operations, functions, conditions, states, events,workflows and other elements (collectively encompassed by the term“states”) of such environments. Data may also include metadata about anyof the foregoing, such as describing data, indicating provenance,indicating elements relating to identity, access, roles, andpermissions, providing summaries or abstractions of data, or otherwiseaugmenting one or more items of data to enable further processing, suchas for extraction, transforming, loading, and processing data. Such data(such term including metadata except where context indicates otherwise)may be highly valuable to third parties, either as an individual element(such as where data about the state of an environment can be used as acondition within a process) or in the aggregate (such as where collecteddata, optionally over many systems and devices in different environmentscan be used to develop models of behavior, to train learning systems, orthe like). As billions of IoT devices are deployed, with countlessconnections, the amount of available data will proliferate. To enableaccess and utilization of data, the cognitive data marketplace 4102enables various components, features, services, and processes forenabling users to supply, find, consume, and transact in packages ofdata, such as batches of data, streams of data (including eventstreams), data from various data pools 4120, and the like. Inembodiments, the cognitive data marketplace 4102 may be included in,connected to, or integrated with, one or more other components of a hostprocessing architecture 4024 of a host processing system 112, such as acloud-based system, as well as to various sensors, input sources 115,data collection systems 102 and the like. The cognitive data marketplace4102 may include marketplace interfaces 4108, which may include one ormore supplier interfaces by which data suppliers may make data availableand one more consumer interfaces by which data may be found andacquired. The consumer interface may include an interface to a datamarket search system 4118, which may include features that enable a userto indicate what types of data a user wishes to obtain, such as byentering keywords in a natural language search interface thatcharacterize data or metadata. The search interface can use varioussearch and filtering techniques, including keyword matching,collaborative filtering (such as using known preferences orcharacteristics of the consumer to match to similar consumers and thepast outcomes of those other consumers), ranking techniques (such asranking based on success of past outcomes according to various metrics,such as those described in connection with other embodiments in thisdisclosure). In embodiments, a supply interface may allow an owner orsupplier of data to supply the data in one or more packages to andthrough the cognitive data marketplace 4102, such as packaging batchesof data, streams of data, or the like. The supplier may pre-packagedata, such as by providing data from a single input source 116, a singlesensor, and the like, or by providing combinations, permutations, andthe like (such as multiplexed analog data, mixed bytes of data frommultiple sources, results of extraction, loading and transformation,results of convolution, and the like), as well as by providing metadatawith respect to any of the foregoing. Packaging may include pricing,such as on a per-batch basis, on a streaming basis (such as subscriptionto an event feed or other feed or stream), on a per item basis, on arevenue share basis, or other basis. For data involving pricing, a datatransaction system 4114 may track orders, delivery, and utilization,including fulfillment of orders. The transaction system 4114 may includerich transaction features, including digital rights management, such asby managing cryptographic keys that govern access control to purchaseddata, that govern usage (such as allowing data to be used for a limitedtime, in a limited domain, by a limited set of users or roles, or for alimited purpose). The transaction system 4114 may manage payments, suchas by processing credit cards, wire transfers, debits, and other formsof consideration.

In embodiments, a cognitive data packaging system 4012 of themarketplace 4102 may use machine-based intelligence to package data,such as by automatically configuring packages of data in batches,streams, pools, or the like. In embodiments, packaging may be accordingto one or more rules, models, or parameters, such as by packaging oraggregating data that is likely to supplement or complement an existingmodel. For example, operating data from a group of similar machines(such as one or more industrial machines noted throughout thisdisclosure) may be aggregated together, such as based on metadataindicating the type of data or by recognizing features orcharacteristics in the data stream that indicate the nature of the data.In embodiments, packaging may occur using machine learning and cognitivecapabilities, such as by learning what combinations, permutations,mixes, layers, and the like of input sources 116, sensors, informationfrom data pools 4120 and information from data collection systems 102are likely to satisfy user requirements or result in measures ofsuccess. Learning may be based on learning feedback 4012, such as basedon measures determined in an analytic system 4018, such as systemperformance measures, data collection measures, analytic measures, andthe like. In embodiments, success measures may be correlated tomarketplace success measures, such as viewing of packages, engagementwith packages, purchase or licensing of packages, payments made forpackages, and the like. Such measures may be calculated in an analyticsystem 4018, including associating particular feedback measures withsearch terms and other inputs, so that the cognitive packaging system4110 can find and configure packages that are designed to provideincreased value to consumers and increased returns for data suppliers.In embodiments, the cognitive data packaging system 4110 canautomatically vary packaging, such as using different combinations,permutations, mixes, and the like, and varying weights applied to giveninput sources, sensors, data pools and the like, using learning feedback4012 to promote favorable packages and de-emphasize less favorablepackages. This may occur using genetic programming and similartechniques that compare outcomes for different packages. Feedback mayinclude state information from the state system 4020 (such as aboutvarious operating states, and the like), as well as about marketplaceconditions and states, such as pricing and availability information forother data sources. Thus, an adaptive cognitive data packaging system4110 is provided that automatically adapts to conditions to providefavorable packages of data for the marketplace 4102.

In embodiments, a cognitive data pricing system 4112 may be provided toset pricing for data packages. In embodiments, the data pricing system4112 may use a set of rules, models, or the like, such as settingpricing based on supply conditions, demand conditions, pricing ofvarious available sources, and the like. For example, pricing for apackage may be configured to be set based on the sum of the prices ofconstituent elements (such as input sources, sensor data, or the like),or to be set based on a rule-based discount to the sum of prices forconstituent elements, or the like. Rules and conditional logic may beapplied, such as rules that factor in cost factors (such as bandwidthand network usage, peak demand factors, scarcity factors, and the like),rules that factor in utilization parameters (such as the purpose,domain, user, role, duration, or the like for a package) and manyothers. In embodiments, the cognitive data pricing system 4112 mayinclude fully cognitive, intelligent features, such as using geneticprogramming including automatically varying pricing and trackingfeedback on outcomes. Outcomes on which tracking feedback may be basedinclude various financial yield metrics, utilization metrics and thelike that may be provided by calculating metrics in an analytic system4018 on data from the data transaction system 4114.

Methods and systems are disclosed herein for self-organizing data poolswhich may include self-organization of data pools based on utilizationand/or yield metrics, including utilization and/or yield metrics thatare tracked for a plurality of data pools. The data pools may initiallycomprise unstructured or loosely structured pools of data that containdata from industrial environments, such as sensor data from or aboutindustrial machines or components. For example, a data pool might takestreams of data from various machines or components in an environment,such as turbines, compressors, batteries, reactors, engines, motors,vehicles, pumps, rotors, axles, bearings, valves, and many others, withthe data streams containing analog and/or digital sensor data (of a widerange of types), data published about operating conditions, diagnosticand fault data, identifying data for machines or components, assettracking data, and many other types of data. Each stream may have anidentifier in the pool, such as indicating its source, and optionallyits type. The data pool may be accessed by external systems, such asthrough one or more interfaces or APIs (e.g., RESTful APIs), or by dataintegration elements (such as gateways, brokers, bridges, connectors, orthe like), and the data pool may use similar capabilities to get accessto available data streams. A data pool may be managed by aself-organizing machine learning facility, which may configure the datapool, such as by managing what sources are used for the pool, managingwhat streams are available, and managing APIs or other connections intoand out of the data pool. The self-organization may take feedback suchas based on measures of success that may include measures of utilizationand yield. The measures of utilization and yield that may include mayaccount for the cost of acquiring and/or storing data, as well as thebenefits of the pool, measured either by profit or by other measuresthat may include user indications of usefulness, and the like. Forexample, a self-organizing data pool might recognize that chemical andradiation data for an energy production environment are regularlyaccessed and extracted, while vibration and temperature data have notbeen used, in which case the data pool might automatically reorganize,such as by ceasing storage of vibration and/or temperature data, or byobtaining better sources of such data. This automated reorganization canalso apply to data structures, such as promoting different data types,different data sources, different data structures, and the like, throughprogressive iteration and feedback.

In embodiments, a platform is provided having self-organization of datapools based on utilization and/or yield metrics. In embodiments, thedata pools 4020 may be self-organizing data pools 4020, such as beingorganized by cognitive capabilities as described throughout thisdisclosure. The data pools 4020 may self-organize in response tolearning feedback 4012, such as based on feedback of measures andresults, including calculated in an analytic system 4018. Organizationmay include determining what data or packages of data to store in a pool(such as representing particular combinations, permutations,aggregations, and the like), the structure of such data (such as inflat, hierarchical, linked, or other structures), the duration ofstorage, the nature of storage media (such as hard disks, flash memory,SSDs, network-based storage, or the like), the arrangement of storagebits, and other parameters. The content and nature of storage may bevaried, such that a data pool 4020 may learn and adapt, such as based onstates of the host system 112, one or more data collection systems 102,storage environment parameters (such as capacity, cost, and performancefactors), data collection environment parameters, marketplaceparameters, and many others. In embodiments, pools 4020 may learn andadapt, such as by variation of the above and other parameters inresponse to yield metrics (such as return on investment, optimization ofpower utilization, optimization of revenue, and the like).

Methods and systems are disclosed herein for training AI models based onindustry-specific feedback, including training an AI model based onindustry-specific feedback that reflects a measure of utilization,yield, or impact, and where the AI model operates on sensor data from anindustrial environment. As noted above, these models may includeoperating models for industrial environments, machines, workflows,models for anticipating states, models for predicting fault andoptimizing maintenance, models for self-organizing storage (on devices,in data pools and/or in the cloud), models for optimizing data transport(such as for optimizing network coding, network-condition-sensitiverouting, and the like), models for optimizing data marketplaces, andmany others.

In embodiments, a platform is provided having training AI models basedon industry-specific feedback. In embodiments, the various embodimentsof cognitive systems disclosed herein may take inputs and feedback fromindustry-specific and domain-specific sources 116 (such as relating tooptimization of specific machines, devices, components, processes, andthe like). Thus, learning and adaptation of storage organization,network usage, combination of sensor and input data, data pooling, datapackaging, data pricing, and other features (such as for a marketplace4102 or for other purposes of the host processing system 112) may beconfigured by learning on the domain-specific feedback measures of agiven environment or application, such as an application involving IoTdevices (such as an industrial environment). This may includeoptimization of efficiency (such as in electrical, electromechanical,magnetic, physical, thermodynamic, chemical and other processes andsystems), optimization of outputs (such as for production of energy,materials, products, services and other outputs), prediction, avoidanceand mitigation of faults (such as in the aforementioned systems andprocesses), optimization of performance measures (such as returns oninvestment, yields, profits, margins, revenues and the like), reductionof costs (including labor costs, bandwidth costs, data costs, materialinput costs, licensing costs, and many others), optimization of benefits(such as relating to safety, satisfaction, health), optimization of workflows (such as optimizing time and resource allocation to processes),and others.

Methods and systems are disclosed herein for a self-organized swarm ofindustrial data collectors, including a self-organizing swarm ofindustrial data collectors that organize among themselves to optimizedata collection based on the capabilities and conditions of the membersof the swarm. Each member of the swarm may be configured withintelligence, and the ability to coordinate with other members. Forexample, a member of the swarm may track information about what dataother members are handling, so that data collection activities, datastorage, data processing, and data publishing can be allocatedintelligently across the swarm, taking into account conditions of theenvironment, capabilities of the members of the swarm, operatingparameters, rules (such as from a rules engine that governs theoperation of the swarm), and current conditions of the members. Forexample, among four collectors, one that has relatively low currentpower levels (such as a low battery), might be temporarily allocated therole of publishing data, because it may receive a dose of power from areader or interrogation device (such as an RFID reader) when it needs topublish the data. A second collector with good power levels and robustprocessing capability might be assigned more complex functions, such asprocessing data, fusing data, organizing the rest of the swarm(including self-organization under machine learning, such that the swarmis optimized over time, including by adjusting operating parameters,rules, and the like based on feedback), and the like. A third collectorin the swarm with robust storage capabilities might be assigned the taskof collecting and storing a category of data, such as vibration sensordata, that consumes considerable bandwidth. A fourth collector in theswarm, such as one with lower storage capabilities, might be assignedthe role of collecting data that can usually be discarded, such as dataon current diagnostic conditions, where only data on faults needs to bemaintained and passed along. Members of a swarm may connect bypeer-to-peer relationships by using a member as a “master” or “hub,” orby having them connect in a series or ring, where each member passesalong data (including commands) to the next, and is aware of the natureof the capabilities and commands that are suitable for the precedingand/or next member. The swarm may be used for allocation of storageacross it (such as using memory of each memory as an aggregate datastore. In these examples, the aggregate data store may support adistributed ledger, which may store transaction data, such as fortransactions involving data collected by the swarm, transactionsoccurring in the industrial environment, or the like. In embodiments,the transaction data may also include data used to manage the swarm, theenvironment, or a machine or components thereof. The swarm mayself-organize, either by machine learning capability disposed on one ormore members of the swarm, or based on instructions from an externalmachine learning facility, which may optimize storage, data collection,data processing, data presentation, data transport, and other functionsbased on managing parameters that are relevant to each. The machinelearning facility may start with an initial configuration and varyparameters of the swarm relevant to any of the foregoing (also includingvarying the membership of the swarm), such as iterating based onfeedback to the machine learning facility regarding measures of success(such as utilization measures, efficiency measures, measures of successin prediction or anticipation of states, productivity measures, yieldmeasures, profit measures, and others). Over time, the swarm may beoptimized to a favorable configuration to achieve the desired measure ofsuccess for an owner, operator, or host of an industrial environment ora machine, component, or process thereof.

In embodiments, a platform is provided having a self-organized swarm ofindustrial data collectors. In embodiments, a host processing system112, with its processing architecture 4024 (and optionally includingintegration with or inclusion of a cognitive data marketplace 4102) mayintegrate with, connect to, or use information from a self-organizingswarm 4202 of data collectors 102. In embodiments, the self-organizingswarm 4202 may organize (such as through deployment of cognitivefeatures on one or more of the data collection systems 102) two or moredata collection systems 102, such as to provided coordination of theswarm 4202. The swarm 4202 may be organized based on a hierarchicalorganization (such as where a master data collector 102 organizes anddirects activities of one or more subservient data collectors 102), acollaborative organization (such as where decision-making for theorganization of the swarm 4202 is distributed among the data collectors102 (such as using various models for decision-making, such as votingsystems, points systems, least-cost routing systems, prioritizationsystems, and the like, and the like. In embodiments, one or more of thedata collectors 102 may have mobility capabilities, such as in caseswhere a data collector is disposed on or in a mobile robot, drone,mobile submersible, or the like, so that organization may include thelocation and positioning of the data collectors 102. Data collectionsystems 102 may communicate with each other and with the host processingsystem 112, including sharing an aggregate allocated storage spaceinvolving storage on or accessible to one or more of the collectors(which in embodiment may be treated as a unified storage space even ifphysically distributed, such as using virtualization capabilities).Organization may be automated based on one or more rules, models,conditions, processes, or the like (such as embodied or executed byconditional logic), and organization may be governed by policies, suchas handled by the policy engine. Rules may be based on industry,application- and domain-specific objects, classes, events, workflows,processes, and systems, such as by setting up the swarm 4202 to collectselected types of data at designated places and times, such ascoordinated with the foregoing. For example, the swarm 4202 may assigndata collectors 102 to serially collect diagnostic, sensor,instrumentation and/or telematic data from each of a series of machinesthat execute an industrial process (such as a robotic manufacturingprocess), such as at the time and location of the input to and outputfrom each of those machines. In embodiments, self-organization may becognitive, such as where the swarm varies one or more collectionparameters and adapts the selection of parameters, weights applied tothe parameters, or the like, over time. In examples, this may be inresponse to learning and feedback, such as from the learning feedbacksystem 4012 that may be based on various feedback measures that may bedetermined by applying the analytic system 4018 (which in embodimentsmay reside on the swarm 4202, the host processing system 112, or acombination thereof) to data handled by the swarm 4202 or to otherelements of the various embodiments disclosed herein (includingmarketplace elements and others). Thus, the swarm 4202 may displayadaptive behavior, such as adapting to the current state 4020 or ananticipated state of its environment (accounting for marketplacebehavior), behavior of various objects (such as IoT devices, machines,components, and systems), processes (including events, states,workflows, and the like), and other factors at a given time. Parametersthat may be varied in a process of variation (such as in a neural net,self-organizing map, or the like), selection, promotion, or the like(such as enabled by genetic programming or other AI-based techniques).Parameters that may be managed, varied, selected and adapted bycognitive, machine learning may include storage parameters (location,type, duration, amount, structure and the like across the swarm 4202),network parameters (such as how the swarm 4202 is organized, such as inmesh, peer-to-peer, ring, serial, hierarchical and other networkconfigurations as well as bandwidth utilization, data routing, networkprotocol selection, network coding type, and other networkingparameters), security parameters (such as settings for various securityapplications and services), location and positioning parameters (such asrouting movement of mobile data collectors 102 to locations, positioningand orienting collectors 102 and the like relative to points of dataacquisition, relative to each other, and relative to locations wherenetwork availability may be favorable, among others), input selectionparameters (such as input selection among sensors, input sources 116 andthe like for each collector 102 and for the aggregate collection), datacombination parameters (such as for sensor fusion, input combination,multiplexing, mixing, layering, convolution, and other combinations),power parameters (such as based on power levels and power availabilityfor one or more collectors 102 or other objects, devices, or the like),states (including anticipated states and conditions of the swarm 4202,individual collection systems 102, the host processing system 112 or oneor more objects in an environment), events, and many others. Feedbackmay be based on any of the kinds of feedback described herein, such thatover time the swarm may adapt to its current and anticipated situationto achieve a wide range of desired objectives.

Methods and systems are disclosed herein for an industrial IoTdistributed ledger, including a distributed ledger supporting thetracking of transactions executed in an automated data marketplace forindustrial IoT data. A distributed ledger may distribute storage acrossdevices, using a secure protocol, such as ones used for cryptocurrencies(such as the Blockchain™ protocol used to support the Bitcoin™currency). A ledger or similar transaction record, which may comprise astructure where each successive member of a chain stores data forprevious transactions, and a competition can be established to determinewhich of alternative data stored data structures is “best” (such asbeing most complete), can be stored across data collectors, industrialmachines or components, data pools, data marketplaces, cloud computingelements, servers, and/or on the IT infrastructure of an enterprise(such as an owner, operator or host of an industrial environment or ofthe systems disclosed herein). The ledger or transaction may beoptimized by machine learning, such as to provide storage efficiency,security, redundancy, or the like.

In embodiments, the cognitive data marketplace 4102 may use a securearchitecture for tracking and resolving transactions, such as adistributed ledger 4004, wherein transactions in data packages aretracked in a chained, distributed data structure, such as a Blockchain™,allowing forensic analysis and validation where individual devices storea portion of the ledger representing transactions in data packages. Thedistributed ledger 4004 may be distributed to IoT devices, to data pools4020, to data collection systems 102, and the like, so that transactioninformation can be verified without reliance on a single, centralrepository of information. The transaction system 4114 may be configuredto store data in the distributed ledger 4004 and to retrieve data fromit (and from constituent devices) in order to resolve transactions.Thus, a distributed ledger 4004 for handling transactions in data, suchas for packages of IoT data, is provided. In embodiments, theself-organizing storage system 4028 may be used for optimizing storageof distributed ledger data, as well as for organizing storage ofpackages of data, such as IoT data, that can be presented in themarketplace 4102.

Methods and systems are disclosed herein for a self-organizingcollector, including a self-organizing, multi-sensor data collector thatcan optimize data collection, power and/or yield based on conditions inits environment. The collector may, for example, organize datacollection by turning on and off particular sensors, such as based onpast utilization patterns or measures of success, as managed by amachine learning facility that iterates configurations and tracksmeasures of success. For example, a multi-sensor collector may learn toturn off certain sensors when power levels are low or during timeperiods where utilization of the data from such sensors is low, or viceversa. Self-organization can also automatically organize how data iscollected (which sensors, from what external sources), how data isstored (at what level of granularity or compression, for how long,etc.), how data is presented (such as in fused or multiplexedstructures, in byte-like structures, or in intermediate statisticalstructures). This may be improved over time, from an initialconfiguration, by training the self-organizing facility based on datasets from real operating environments, such as based on feedbackmeasures, including many of the types of feedback described throughoutthis disclosure.

Methods and systems are disclosed herein for a network-sensitivecollector, including a network condition-sensitive, self-organizing,multi-sensor data collector that can optimize based on bandwidth,quality of service, pricing and/or other network conditions. Networksensitivity can include awareness of the price of data transport (suchas allowing the system to pull or push data during off-peak periods orwithin the available parameters of paid data plans), the quality of thenetwork (such as to avoid periods where errors are likely), the qualityof environmental conditions (such as delaying transmission until signalquality is good, such as when a collector emerges from a shieldedenvironment, avoiding wasting use of power when seeking a signal whenshielded, such as by large metal structures typically of industrialenvironments), and the like.

Methods and systems are disclosed herein for a remotely organizeduniversal data collector that can power up and down sensor interfacesbased on need and/or conditions identified in an industrial datacollection environment. For example, interfaces can recognize whatsensors are available and interfaces and/or processors can be turned onto take input from such sensors, including hardware interfaces thatallow the sensors to plug in to the data collector, wireless datainterfaces (such as where the collector can ping the sensor, optionallyproviding some power via an interrogation signal), and softwareinterfaces (such as for handling particular types of data). Thus, acollector that is capable of handling various kinds of data can beconfigured to adapt to the particular use in a given environment. Inembodiments, configuration may be automatic or under machine learning,which may improve configuration by optimizing parameters based onfeedback measures over time.

Methods and systems are disclosed herein for self-organizing storage fora multi-sensor data collector, including self-organizing storage for amulti-sensor data collector for industrial sensor data. Self-organizingstorage may allocate storage based on application of machine learning,which may improve storage configuration based on feedback measure overtime. Storage may be optimized by configuring what data types are used(e.g., byte-like structures, structures representing fused data frommultiple sensors, structures representing statistics or measurescalculated by applying mathematical functions on data, and the like) byconfiguring compression, by configuring data storage duration, byconfiguring write strategies (such as by striping data across multiplestorage devices, using protocols where one device stores instructionsfor other devices in a chain, and the like), and by configuring storagehierarchies, such as by providing pre-calculated intermediate statisticsto facilitate more rapid access to frequently accessed data items).Thus, highly intelligent storage systems may be configured andoptimized, based on feedback, over time.

Methods and systems are disclosed herein for self-organizing networkcoding for a multi-sensor data network, including self-organizingnetwork coding for a data network that transports data from multiplesensors in an industrial data collection environment. Network coding,including random linear network coding, can enable highly efficient andreliable transport of large amounts of data over various kinds ofnetworks. Different network coding configurations can be selected, basedon machine learning, to optimize network coding and other networktransport characteristics based on network conditions, environmentalconditions, and other factors, such as the nature of the data beingtransported, environmental conditions, operating conditions, and thelike (including by training a network coding selection model over timebased on feedback of measures of success, such as any of the measuresdescribed herein).

In embodiments, a platform is provided having a self-organizing networkcoding for multi-sensor data network. A cognitive system may vary one ormore parameters for networking, such as network type selection (e.g.,selecting among available local, cellular, satellite, Wi-Fi, Bluetooth,NFC, Zigbee and other networks), network selection (such as selecting aspecific network, such as one that is known to have desired securityfeatures), network coding selection (such as selecting a type of networkcoding for efficient transport), network timing selection (such asconfiguring delivery based on network pricing conditions, traffic andthe like), network feature selection (such as selecting cognitivefeatures, security features, and the like), network conditions (such asnetwork quality based on current environmental or operation conditions),network feature selection (such as enabling available authentication,permission and similar systems), network protocol selection (such asamong HTTP, IP, TCP/IP, cellular, satellite, serial, packet, streaming,and many other protocols), and others. Given bandwidth constraints,price variations, sensitivity to environmental factors, securityconcerns, and the like, selecting the optimal network configuration canbe highly complex and situation dependent. The self-organizingnetworking system 4030 may vary combinations and permutations of theseparameters while taking input from a learning feedback system 4012 suchas using information from the analytic system 4018 about variousmeasures of outcomes. In the many examples, outcomes may include overallsystem measures, analytic success measures, and local performanceindicators. In embodiments, input from a learning feedback system 4012may include information from various sensors and input sources 116,information from the state system 4020 about states (such as events,environmental conditions, operating conditions, and many others), orother information) or taking other inputs. By variation and selection ofalternative configurations of networking parameters in different states,the self-organizing networking system may find configurations that arewell-adapted to the environment that is being monitored or controlled bythe host system 112, such as the one where one or more data collectionsystems 102 are located and that are well-adapted to emerging networkconditions. Thus, a self-organizing, network-condition-adaptive datacollection system is provided.

Referring to FIG. 42, a data collection system 102 may have one or moreoutput interfaces and/or ports 4010. These may include network ports andconnections, application programming interfaces, and the like. Methodsand systems are disclosed herein for a haptic or multi-sensory userinterface, including a wearable haptic or multi-sensory user interfacefor an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs. For example, an interface may, based ona data structure configured to support it, be set up to provide a userwith input or feedback, such as based on data from sensors in theenvironment. For example, if a fault condition based on a vibration data(such as resulting from a bearing being worn down, an axle beingmisaligned, or a resonance condition between machines) is detected, itcan be presented in a haptic interface by vibration of an interface,such as shaking a wrist-worn device. Similarly, thermal data indicatingoverheating could be presented by warming or cooling a wearable device,such as while a worker is working on a machine and cannot necessarilylook at a user interface. Similarly, electrical, or magnetic data may bepresented by a buzzing, and the like, such as to indicate presence of anopen electrical connection or wire, etc. That is, a multi-sensoryinterface can intuitively help a user (such as one wearing a wearabledevice) get a quick indication of what is going on in an environment,with the wearable interface having various modes of interaction that donot require a user to have eyes on a graphical UI, which may bedifficult or impossible in many industrial environments where a userneeds to keep an eye on the environment.

In embodiments, a platform is provided having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs. In embodiments, a haptic user interface4302 is provided as an output for a data collection system 102, such asfor handling and providing information for vibration, heat, electricaland/or sound outputs, such as to one or more components of the datacollection system 102 or to another system, such as a wearable device,mobile phone, or the like. A data collection system 102 may be providedin a form factor suitable for delivering haptic input to a user, such asby vibrating, warming or cooling, buzzing, or the like, such as beingdisposed in headgear, an armband, a wristband or watch, a belt, an itemof clothing, a uniform, or the like. In such cases, data collectionsystems 102 may be integrated with gear, uniforms, equipment, or thelike worn by users, such as individuals responsible for operating ormonitoring an industrial environment. In embodiments, signals fromvarious sensors or input sources (or selective combinations,permutations, mixes, and the like, as managed by one or more of thecognitive input selection systems 4004, 4014) may trigger hapticfeedback. For example, if a nearby industrial machine is overheating,the haptic interface may alert a user by warming up, or by sending asignal to another device (such as a mobile phone) to warm up. If asystem is experiencing unusual vibrations, the haptic interface mayvibrate. Thus, through various forms of haptic input, a data collectionsystem 102 may inform users of the need to attend to one or moredevices, machines, or other factors (such as in an industrialenvironment) without requiring them to read messages or divert theirvisual attention away from the task at hand. The haptic interface, andselection of what outputs should be provided, may be considered in thecognitive input selection systems 4004, 4014. For example, user behavior(such as responses to inputs) may be monitored and analyzed in ananalytic system 4018, and feedback may be provided through the learningfeedback system 4012, so that signals may be provided based on the rightcollection or package of sensors and inputs, at the right time and inthe right manner, to optimize the effectiveness of the haptic system4202. This may include rule-based or model-based feedback (such asproviding outputs that correspond in some logical fashion to the sourcedata that is being conveyed). In embodiments, a cognitive haptic systemmay be provided, where selection of inputs or triggers for hapticfeedback, selection of outputs, timing, intensity levels, durations, andother parameters (or weights applied to them) may be varied in a processof variation, promotion, and selection (such as using geneticprogramming) with feedback based on real world responses to feedback inactual situations or based on results of simulation and testing of userbehavior. Thus, an adaptive haptic interface for a data collectionsystem 102 is provided, which may learn and adapt feedback to satisfyrequirements and to optimize the impact on user behavior, such as foroverall system outcomes, data collection outcomes, analytic outcomes,and the like.

Methods and systems are disclosed herein for a presentation layer forAR/VR industrial glasses, where heat map elements are presented based onpatterns and/or parameters in collected data. Methods and systems aredisclosed herein for condition-sensitive, self-organized tuning of AR/VRinterfaces based on feedback metrics and/or training in industrialenvironments. In embodiments, any of the data, measures, and the likedescribed throughout this disclosure can be presented by visualelements, overlays, and the like for presentation in the AR/VRinterfaces, such as in industrial glasses, on AR/VR interfaces on smartphones or tablets, on AR/VR interfaces on data collectors (which may beembodied in smart phones or tablets), on displays located on machines orcomponents, and/or on displays located in industrial environments.

In embodiments, a platform is provided having heat maps displayingcollected data for AR/VR. In embodiments, a platform is provided havingheat maps 4204 displaying collected data from a data collection system102 for providing input to an AR/VR interface 4208. In embodiments, theheat map interface 4304 is provided as an output for a data collectionsystem 102, such as for handling and providing information forvisualization of various sensor data and other data (such as map data,analog sensor data, and other data), such as to one or more componentsof the data collection system 102 or to another system, such as a mobiledevice, tablet, dashboard, computer, AR/VR device, or the like. A datacollection system 102 may be provided in a form factor suitable fordelivering visual input to a user, such as by presenting a map thatincludes indicators of levels of analog and digital sensor data (such asindicating levels of rotation, vibration, heating or cooling, pressure,and many other conditions). In such cases, data collection systems 102may be integrated with equipment, or the like that are used byindividuals responsible for operating or monitoring an industrialenvironment. In embodiments, signals from various sensors or inputsources (or selective combinations, permutations, mixes, and the like,as managed by one or more of the cognitive input selection systems 4004,4014) may provide input data to a heat map. Coordinates may include realworld location coordinates (such as geo-location or location on a map ofan environment), as well as other coordinates, such as time-basedcoordinates, frequency-based coordinates, or other coordinates thatallow for representation of analog sensor signals, digital signals,input source information, and various combinations, in a map-basedvisualization, such that colors may represent varying levels of inputalong the relevant dimensions. For example, if a nearby industrialmachine is overheating, the heat map interface may alert a user byshowing a machine in bright red. If a system is experiencing unusualvibrations, the heat map interface may show a different color for avisual element for the machine, or it may cause an icon or displayelement representing the machine to vibrate in the interface, callingattention to the element. Clicking, touching, or otherwise interactingwith the map can allow a user to drill down and see underlying sensor orinput data that is used as an input to the heat map display. Thus,through various forms of display, a data collection system 102 mayinform users of the need to attend to one or more devices, machines, orother factors, such as in an industrial environment, without requiringthem to read text-based messages or input. The heat map interface, andselection of what outputs should be provided, may be considered in thecognitive input selection systems 4004, 4014. For example, user behavior(such as responses to inputs or displays) may be monitored and analyzedin an analytic system 4018, and feedback may be provided through thelearning feedback system 4012, so that signals may be provided based onthe right collection or package of sensors and inputs, at the right timeand in the right manner, to optimize the effectiveness of the heat mapUI 4304. This may include rule-based or model-based feedback (such asproviding outputs that correspond in some logical fashion to the sourcedata that is being conveyed). In embodiments, a cognitive heat mapsystem may be provided, where selection of inputs or triggers for heatmap displays, selection of outputs, colors, visual representationelements, timing, intensity levels, durations and other parameters (orweights applied to them) may be varied in a process of variation,promotion and selection (such as using genetic programming) withfeedback based on real world responses to feedback in actual situationsor based on results of simulation and testing of user behavior. Thus, anadaptive heat map interface for a data collection system 102, or datacollected thereby 102, or data handled by a host processing system 112,is provided, which may learn and adapt feedback to satisfy requirementsand to optimize the impact on user behavior and reaction, such as foroverall system outcomes, data collection outcomes, analytic outcomes,and the like.

In embodiments, a platform is provided having automatically tuned AR/VRvisualization of data collected by a data collector. In embodiments, aplatform is provided having an automatically tuned AR/VR visualizationsystem 4308 for visualization of data collected by a data collectionsystem 102, such as where the data collection system 102 has an AR/VRinterface 4208 or provides input to an AR/VR interface 4308 (such as amobile phone positioned in a virtual reality or AR headset, a set of ARglasses, or the like). In embodiments, the AR/VR system 4308 is providedas an output interface of a data collection system 102, such as forhandling and providing information for visualization of various sensordata and other data (such as map data, analog sensor data, and otherdata), such as to one or more components of the data collection system102 or to another system, such as a mobile device, tablet, dashboard,computer, AR/VR device, or the like. A data collection system 102 may beprovided in a form factor suitable for delivering AR or VR visual,auditory, or other sensory input to a user, such as by presenting one ormore displays (such as 3D-realistic visualizations, objects, maps,camera overlays, or other overlay elements, maps and the like thatinclude or correspond to indicators of levels of analog and digitalsensor data (such as indicating levels of rotation, vibration, heatingor cooling, pressure and many other conditions, to input sources 116, orthe like). In such cases, data collection systems 102 may be integratedwith equipment, or the like that are used by individuals responsible foroperating or monitoring an industrial environment.

In embodiments, signals from various sensors or input sources (orselective combinations, permutations, mixes, and the like as managed byone or more of the cognitive input selection systems 4004, 4014) mayprovide input data to populate, configure, modify, or otherwisedetermine the AR/VR element. Visual elements may include a wide range oficons, map elements, menu elements, sliders, toggles, colors, shapes,sizes, and the like, for representation of analog sensor signals,digital signals, input source information, and various combinations. Inmany examples, colors, shapes, and sizes of visual overlay elements mayrepresent varying levels of input along the relevant dimensions for asensor or combination of sensors. In further examples, if a nearbyindustrial machine is overheating, an AR element may alert a user byshowing an icon representing that type of machine in flashing red colorin a portion of the display of a pair of AR glasses. If a system isexperiencing unusual vibrations, a virtual reality interface showingvisualization of the components of the machine (such as overlaying acamera view of the machine with 3D visualization elements) may show avibrating component in a highlighted color, with motion, or the like, sothat it stands out in a virtual reality environment being used to help auser monitor or service the machine. Clicking, touching, moving eyestoward, or otherwise interacting with a visual element in an AR/VRinterface may allow a user to drill down and see underlying sensor orinput data that is used as an input to the display. Thus, throughvarious forms of display, a data collection system 102 may inform usersof the need to attend to one or more devices, machines, or other factors(such as in an industrial environment), without requiring them to readtext-based messages or input or divert attention from the applicableenvironment (whether it is a real environment with AR features or avirtual environment, such as for simulation, training, or the like).

The AR/VR output interface 4208, and selection and configuration of whatoutputs or displays should be provided, may be handled in the cognitiveinput selection systems 4004, 4014. For example, user behavior (such asresponses to inputs or displays) may be monitored and analyzed in ananalytic system 4018, and feedback may be provided through the learningfeedback system 4012, so that AR/VR display signals may be providedbased on the right collection or package of sensors and inputs, at theright time and in the right manner, to optimize the effectiveness of theAR/VR UI 4308. This may include rule-based or model-based feedback (suchas providing outputs that correspond in some logical fashion to thesource data that is being conveyed). In embodiments, a cognitively tunedAR/VR interface control system 4308 may be provided, where selection ofinputs or triggers for AR/VR display elements, selection of outputs(such as colors, visual representation elements, timing, intensitylevels, durations and other parameters) and other parameters of a VR/ARenvironment may be varied in a process of variation, promotion andselection (such as using genetic programming) with feedback based onreal world responses in actual situations or based on results ofsimulation and testing of user behavior. Thus, an adaptive, tuned AR/VRinterface for a data collection system 102, or data collected thereby102, or data handled by a host processing system 112, is provided, whichmay learn and adapt feedback to satisfy requirements and to optimize theimpact on user behavior and reaction, such as for overall systemoutcomes, data collection outcomes, analytic outcomes, and the like.

As noted above, methods and systems are disclosed herein for continuousultrasonic monitoring, including providing continuous ultrasonicmonitoring of rotating elements and bearings of an energy productionfacility. Embodiments include using continuous ultrasonic monitoring ofan industrial environment as a source for a cloud-deployed patternrecognizer. Embodiments include using continuous ultrasonic monitoringto provide updated state information to a state machine that is used asan input to a cloud-based pattern recognizer. Embodiments include makingavailable continuous ultrasonic monitoring information to a user basedon a policy declared in a policy engine. Embodiments include storingultrasonic continuous monitoring data with other data in a fused datastructure on an industrial sensor device. Embodiments include making astream of continuous ultrasonic monitoring data from an industrialenvironment available as a service from a data marketplace. Embodimentsinclude feeding a stream of continuous ultrasonic data into aself-organizing data pool. Embodiments include training a machinelearning model to monitor a continuous ultrasonic monitoring data streamwhere the model is based on a training set created from human analysisof such a data stream, and is improved based on data collected onperformance in an industrial environment. Embodiments include a swarm ofdata collectors that include at least one data collector for continuousultrasonic monitoring of an industrial environment and at least oneother type of data collector. Embodiments include using a distributedledger to store time-series data from continuous ultrasonic monitoringacross multiple devices. Embodiments include collecting a stream ofcontinuous ultrasonic data in a self-organizing data collector.Embodiments include collecting a stream of continuous ultrasonic data ina network-sensitive data collector.

Embodiments include collecting a stream of continuous ultrasonic data ina remotely organized data collector. Embodiments include collecting astream of continuous ultrasonic data in a data collector havingself-organized storage. Embodiments include using self-organizingnetwork coding to transport a stream of ultrasonic data collected froman industrial environment. Embodiments include conveying an indicator ofa parameter of a continuously collected ultrasonic data stream via asensory interface of a wearable device. Embodiments include conveying anindicator of a parameter of a continuously collected ultrasonic datastream via a heat map visual interface of a wearable device. Embodimentsinclude conveying an indicator of a parameter of a continuouslycollected ultrasonic data stream via an interface that operates withself-organized tuning of the interface layer.

As noted above, methods and systems are disclosed herein forcloud-based, machine pattern recognition based on fusion of remote,analog industrial sensors. Embodiments include taking input from aplurality of analog sensors disposed in an industrial environment,multiplexing the sensors into a multiplexed data stream, feeding thedata stream into a cloud-deployed machine learning facility, andtraining a model of the machine learning facility to recognize a definedpattern associated with the industrial environment. Embodiments includeusing a cloud-based pattern recognizer on input states from a statemachine that characterizes states of an industrial environment.Embodiments include deploying policies by a policy engine that governwhat data can be used by what users and for what purpose in cloud-based,machine learning. Embodiments include feeding inputs from multipledevices that have fused, on-device storage of multiple sensor streamsinto a cloud-based pattern recognizer. Embodiments include making anoutput from a cloud-based machine pattern recognizer that analyzes fuseddata from remote, analog industrial sensors available as a data servicein a data marketplace. Embodiments include using a cloud-based platformto identify patterns in data across a plurality of data pools thatcontain data published from industrial sensors. Embodiments includetraining a model to identify preferred sensor sets to diagnose acondition of an industrial environment, where a training set is createdby a human user and the model is improved based on feedback from datacollected about conditions in an industrial environment.

Embodiments include a swarm of data collectors that is governed by apolicy that is automatically propagated through the swarm. Embodimentsinclude using a distributed ledger to store sensor fusion informationacross multiple devices. Embodiments include feeding input from a set ofself-organizing data collectors into a cloud-based pattern recognizerthat uses data from multiple sensors for an industrial environment.Embodiments include feeding input from a set of network-sensitive datacollectors into a cloud-based pattern recognizer that uses data frommultiple sensors from the industrial environment. Embodiments includefeeding input from a set of remotely organized data collectors into acloud-based pattern recognizer that determines user data from multiplesensors from the industrial environment. Embodiments include feedinginput from a set of data collectors having self-organized storage into acloud-based pattern recognizer that uses data from multiple sensors fromthe industrial environment. Embodiments include a system for datacollection in an industrial environment with self-organizing networkcoding for data transport of data fused from multiple sensors in theenvironment. Embodiments include conveying information formed by fusinginputs from multiple sensors in an industrial data collection system ina multi-sensory interface. Embodiments include conveying informationformed by fusing inputs from multiple sensors in an industrial datacollection system in a heat map interface. Embodiments include conveyinginformation formed by fusing inputs from multiple sensors in anindustrial data collection system in an interface that operates withself-organized tuning of the interface layer.

As noted above, methods and systems are disclosed herein forcloud-based, machine pattern analysis of state information from multipleanalog industrial sensors to provide anticipated state information foran industrial system. Embodiments include providing cloud-based patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system.Embodiments include using a policy engine to determine what stateinformation can be used for cloud-based machine analysis. Embodimentsinclude feeding inputs from multiple devices that have fused andon-device storage of multiple sensor streams into a cloud-based patternrecognizer to determine an anticipated state of an industrialenvironment. Embodiments include making anticipated state informationfrom a cloud-based machine pattern recognizer that analyzes fused datafrom remote, analog industrial sensors available as a data service in adata marketplace. Embodiments include using a cloud-based patternrecognizer to determine an anticipated state of an industrialenvironment based on data collected from data pools that contain streamsof information from machines in the environment. Embodiments includetraining a model to identify preferred state information to diagnose acondition of an industrial environment, where a training set is createdby a human user and the model is improved based on feedback from datacollected about conditions in an industrial environment. Embodimentsinclude a swarm of data collectors that feeds a state machine thatmaintains current state information for an industrial environment.Embodiments include using a distributed ledger to store historical stateinformation for fused sensor states a self-organizing data collectorthat feeds a state machine that maintains current state information foran industrial environment. Embodiments include a network-sensitive datacollector that feeds a state machine that maintains current stateinformation for an industrial environment. Embodiments include aremotely organized data collector that feeds a state machine thatmaintains current state information for an industrial environment.Embodiments include a data collector with self-organized storage thatfeeds a state machine that maintains current state information for anindustrial environment. Embodiments include a system for data collectionin an industrial environment with self-organizing network coding fordata transport and maintains anticipated state information for theenvironment. Embodiments include conveying anticipated state informationdetermined by machine learning in an industrial data collection systemin a multi-sensory interface. Embodiments include conveying anticipatedstate information determined by machine learning in an industrial datacollection system in a heat map interface. Embodiments include conveyinganticipated state information determined by machine learning in anindustrial data collection system in an interface that operates withself-organized tuning of the interface layer.

As noted above, methods and systems are disclosed herein for acloud-based policy automation engine for IoT, with creation, deployment,and management of IoT devices, including a cloud-based policy automationengine for IoT, enabling creation, deployment and management of policiesthat apply to IoT devices. Embodiments include deploying a policyregarding data usage to an on-device storage system that stores fuseddata from multiple industrial sensors. Embodiments include deploying apolicy relating to what data can be provided to whom in aself-organizing marketplace for IoT sensor data. Embodiments includedeploying a policy across a set of self-organizing pools of data thatcontain data streamed from industrial sensing devices to govern use ofdata from the pools. Embodiments include training a model to determinewhat policies should be deployed in an industrial data collectionsystem. Embodiments include deploying a policy that governs how aself-organizing swarm should be organized for a particular industrialenvironment. Embodiments include storing a policy on a device thatgoverns use of storage capabilities of the device for a distributedledger. Embodiments include deploying a policy that governs how aself-organizing data collector should be organized for a particularindustrial environment. Embodiments include deploying a policy thatgoverns how a network-sensitive data collector should use networkbandwidth for a particular industrial environment. Embodiments includedeploying a policy that governs how a remotely organized data collectorshould collect, and make available, data relating to a specifiedindustrial environment. Embodiments include deploying a policy thatgoverns how a data collector should self-organize storage for aparticular industrial environment. Embodiments include a system for datacollection in an industrial environment with a policy engine fordeploying policy within the system and self-organizing network codingfor data transport. Embodiments include a system for data collection inan industrial environment with a policy engine for deploying a policywithin the system, where a policy applies to how data will be presentedin a multi-sensory interface. Embodiments include a system for datacollection in an industrial environment with a policy engine fordeploying a policy within the system, where a policy applies to how datawill be presented in a heat map visual interface. Embodiments include asystem for data collection in an industrial environment with a policyengine for deploying a policy within the system, where a policy appliesto how data will be presented in an interface that operates withself-organized tuning of the interface layer.

As noted above, methods and systems are disclosed herein for on-devicesensor fusion and data storage for industrial IoT devices, includingon-device sensor fusion and data storage for an industrial IoT device,where data from multiple sensors is multiplexed at the device forstorage of a fused data stream. Embodiments include a self-organizingmarketplace that presents fused sensor data that is extracted fromon-device storage of IoT devices. Embodiments include streaming fusedsensor information from multiple industrial sensors and from anon-device data storage facility to a data pool. Embodiments includetraining a model to determine what data should be stored on a device ina data collection environment. Embodiments include a self-organizingswarm of industrial data collectors that organize among themselves tooptimize data collection, where at least some of the data collectorshave on-device storage of fused data from multiple sensors. Embodimentsinclude storing distributed ledger information with fused sensorinformation on an industrial IoT device. Embodiments include on-devicesensor fusion and data storage for a self-organizing industrial datacollector. Embodiments include on-device sensor fusion and data storagefor a network-sensitive industrial data collector. Embodiments includeon-device sensor fusion and data storage for a remotely organizedindustrial data collector. Embodiments include on-device sensor fusionand self-organizing data storage for an industrial data collector.Embodiments include a system for data collection in an industrialenvironment with on-device sensor fusion and self-organizing networkcoding for data transport. Embodiments include a system for datacollection with on-device sensor fusion of industrial sensor data, wheredata structures are stored to support alternative, multi-sensory modesof presentation. Embodiments include a system for data collection withon-device sensor fusion of industrial sensor data, where data structuresare stored to support visual heat map modes of presentation. Embodimentsinclude a system for data collection with on-device sensor fusion ofindustrial sensor data, where data structures are stored to support aninterface that operates with self-organized tuning of the interfacelayer.

As noted above, methods and systems are disclosed herein for aself-organizing data marketplace for industrial IoT data, including aself-organizing data marketplace for industrial IoT data, whereavailable data elements are organized in the marketplace for consumptionby consumers based on training a self-organizing facility with atraining set and feedback from measures of marketplace success.Embodiments include organizing a set of data pools in a self-organizingdata marketplace based on utilization metrics for the data pools.Embodiments include training a model to determine pricing for data in adata marketplace. Embodiments include feeding a data marketplace withdata streams from a self-organizing swarm of industrial data collectors.Embodiments include using a distributed ledger to store transactionaldata for a self-organizing marketplace for industrial IoT data.Embodiments include feeding a data marketplace with data streams fromself-organizing industrial data collectors. Embodiments include feedinga data marketplace with data streams from a set of network-sensitiveindustrial data collectors. Embodiments include feeding a datamarketplace with data streams from a set of remotely organizedindustrial data collectors. Embodiments include feeding a datamarketplace with data streams from a set of industrial data collectorsthat have self-organizing storage. Embodiments include usingself-organizing network coding for data transport to a marketplace forsensor data collected in industrial environments. Embodiments includeproviding a library of data structures suitable for presenting data inalternative, multi-sensory interface modes in a data marketplace.Embodiments include providing a library in a data marketplace of datastructures suitable for presenting data in heat map visualization.Embodiments include providing a library in a data marketplace of datastructures suitable for presenting data in interfaces that operate withself-organized tuning of the interface layer.

As noted above, methods and systems are disclosed herein forself-organizing data pools, including self-organization of data poolsbased on utilization and/or yield metrics, including utilization and/oryield metrics that are tracked for a plurality of data pools.Embodiments include training a model to present the most valuable datain a data marketplace, where training is based on industry-specificmeasures of success. Embodiments include populating a set ofself-organizing data pools with data from a self-organizing swarm ofdata collectors. Embodiments include using a distributed ledger to storetransactional information for data that is deployed in data pools, wherethe distributed ledger is distributed across the data pools. Embodimentsinclude self-organizing of data pools based on utilization and/or yieldmetrics that are tracked for a plurality of data pools, where the poolscontain data from self-organizing data collectors. Embodiments includepopulating a set of self-organizing data pools with data from a set ofnetwork-sensitive data collectors. Embodiments include populating a setof self-organizing data pools with data from a set of remotely organizeddata collectors. Embodiments include populating a set of self-organizingdata pools with data from a set of data collectors havingself-organizing storage. Embodiments include a system for datacollection in an industrial environment with self-organizing pools fordata storage and self-organizing network coding for data transport.Embodiments include a system for data collection in an industrialenvironment with self-organizing pools for data storage that include asource data structure for supporting data presentation in amulti-sensory interface. Embodiments include a system for datacollection in an industrial environment with self-organizing pools fordata storage that include a source data structure for supporting datapresentation in a heat map interface. Embodiments include a system fordata collection in an industrial environment with self-organizing poolsfor data storage that include source a data structure for supportingdata presentation in an interface that operates with self-organizedtuning of the interface layer.

As noted above, methods and systems are disclosed herein for training AImodels based on industry-specific feedback, including training an AImodel based on industry-specific feedback that reflects a measure ofutilization, yield, or impact, where the AI model operates on sensordata from an industrial environment. Embodiments include training aswarm of data collectors based on industry-specific feedback.Embodiments include training an AI model to identify and use availablestorage locations in an industrial environment for storing distributedledger information. Embodiments include training a swarm ofself-organizing data collectors based on industry-specific feedback.Embodiments include training a network-sensitive data collector based onnetwork and industrial conditions in an industrial environment.Embodiments include training a remote organizer for a remotely organizeddata collector based on industry-specific feedback measures. Embodimentsinclude training a self-organizing data collector to configure storagebased on industry-specific feedback. Embodiments include a system fordata collection in an industrial environment with cloud-based trainingof a network coding model for organizing network coding for datatransport. Embodiments include a system for data collection in anindustrial environment with cloud-based training of a facility thatmanages presentation of data in a multi-sensory interface. Embodimentsinclude a system for data collection in an industrial environment withcloud-based training of a facility that manages presentation of data ina heat map interface. Embodiments include a system for data collectionin an industrial environment with cloud-based training of a facilitythat manages presentation of data in an interface that operates withself-organized tuning of the interface layer.

As noted above, methods and systems are disclosed herein for aself-organized swarm of industrial data collectors, including aself-organizing swarm of industrial data collectors that organize amongthemselves to optimize data collection based on the capabilities andconditions of the members of the swarm. Embodiments include deployingdistributed ledger data structures across a swarm of data. Embodimentsinclude a self-organizing swarm of self-organizing data collectors fordata collection in industrial environments. Embodiments include aself-organizing swarm of network-sensitive data collectors for datacollection in industrial environments. Embodiments include aself-organizing swarm of network-sensitive data collectors for datacollection in industrial environments, where the swarm is alsoconfigured for remote organization. Embodiments include aself-organizing swarm of data collectors having self-organizing storagefor data collection in industrial environments. Embodiments include asystem for data collection in an industrial environment with aself-organizing swarm of data collectors and self-organizing networkcoding for data transport. Embodiments include a system for datacollection in an industrial environment with a self-organizing swarm ofdata collectors that relay information for use in a multi-sensoryinterface. Embodiments include a system for data collection in anindustrial environment with a self-organizing swarm of data collectorsthat relay information for use in a heat map interface. Embodimentsinclude a system for data collection in an industrial environment with aself-organizing swarm of data collectors that relay information for usein an interface that operates with self-organized tuning of theinterface layer.

As noted above, methods and systems are disclosed herein for anindustrial IoT distributed ledger, including a distributed ledgersupporting the tracking of transactions executed in an automated datamarketplace for industrial IoT data. Embodiments include aself-organizing data collector that is configured to distributecollected information to a distributed ledger. Embodiments include anetwork-sensitive data collector that is configured to distributecollected information to a distributed ledger based on networkconditions. Embodiments include a remotely organized data collector thatis configured to distribute collected information to a distributedledger based on intelligent, remote management of the distribution.Embodiments include a data collector with self-organizing local storagethat is configured to distribute collected information to a distributedledger. Embodiments include a system for data collection in anindustrial environment using a distributed ledger for data storage andself-organizing network coding for data transport. Embodiments include asystem for data collection in an industrial environment using adistributed ledger for data storage of a data structure supporting ahaptic interface for data presentation. Embodiments include a system fordata collection in an industrial environment using a distributed ledgerfor data storage of a data structure supporting a heat map interface fordata presentation. Embodiments include a system for data collection inan industrial environment using a distributed ledger for data storage ofa data structure supporting an interface that operates withself-organized tuning of the interface layer.

As noted above, methods and systems are disclosed herein for aself-organizing collector, including a self-organizing, multi-sensordata collector that can optimize data collection, power and/or yieldbased on conditions in its environment. Embodiments include aself-organizing data collector that organizes at least in part based onnetwork conditions. Embodiments include a self-organizing data collectorthat is also responsive to remote organization. Embodiments include aself-organizing data collector with self-organizing storage for datacollected in an industrial data collection environment. Embodimentsinclude a system for data collection in an industrial environment withself-organizing data collection and self-organizing network coding fordata transport. Embodiments include a system for data collection in anindustrial environment with a self-organizing data collector that feedsa data structure supporting a haptic or multi-sensory wearable interfacefor data presentation. Embodiments include a system for data collectionin an industrial environment with a self-organizing data collector thatfeeds a data structure supporting a heat map interface for datapresentation. Embodiments include a system for data collection in anindustrial environment with a self-organizing data collector that feedsa data structure supporting an interface that operates withself-organized tuning of the interface layer.

As noted above, methods and systems are disclosed herein for anetwork-sensitive collector, including a network condition-sensitive,self-organizing, multi-sensor data collector that can optimize based onbandwidth, quality of service, pricing and/or other network conditions.Embodiments include a remotely organized, network condition-sensitiveuniversal data collector that can power up and down sensor interfacesbased on need and/or conditions identified in an industrial datacollection environment, including network conditions. Embodimentsinclude a network-condition sensitive data collector withself-organizing storage for data collected in an industrial datacollection environment. Embodiments include a network-conditionsensitive data collector with self-organizing network coding for datatransport in an industrial data collection environment. Embodimentsinclude a system for data collection in an industrial environment with anetwork-sensitive data collector that relays a data structure supportinga haptic wearable interface for data presentation. Embodiments include asystem for data collection in an industrial environment with anetwork-sensitive data collector that relays a data structure supportinga heat map interface for data presentation. Embodiments include a systemfor data collection in an industrial environment with anetwork-sensitive data collector that relays a data structure supportingan interface that operates with self-organized tuning of the interfacelayer.

As noted above, methods and systems are disclosed herein for a remotelyorganized universal data collector that can power up and down sensorinterfaces based on need and/or conditions identified in an industrialdata collection environment. Embodiments include a remotely organizeduniversal data collector with self-organizing storage for data collectedin an industrial data collection environment. Embodiments include asystem for data collection in an industrial environment with remotecontrol of data collection and self-organizing network coding for datatransport. Embodiments include a remotely organized data collector forstoring sensor data and delivering instructions for use of the data in ahaptic or multi-sensory wearable interface. Embodiments include aremotely organized data collector for storing sensor data and deliveringinstructions for use of the data in a heat map visual interface.Embodiments include a remotely organized data collector for storingsensor data and delivering instructions for use of the data in aninterface that operates with self-organized tuning of the interfacelayer.

As noted above, methods and systems are disclosed herein forself-organizing storage for a multi-sensor data collector, includingself-organizing storage for a multi-sensor data collector for industrialsensor data. Embodiments include a system for data collection in anindustrial environment with self-organizing data storage andself-organizing network coding for data transport. Embodiments include adata collector with self-organizing storage for storing sensor data andinstructions for translating the data for use in a haptic wearableinterface. Embodiments include a data collector with self-organizingstorage for storing sensor data and instructions for translating thedata for use in a heat map presentation interface. Embodiments include adata collector with self-organizing storage for storing sensor data andinstructions for translating the data for use in an interface thatoperates with self-organized tuning of the interface layer.

As noted above, methods and systems are disclosed herein forself-organizing network coding for a multi-sensor data network,including self-organizing network coding for a data network thattransports data from multiple sensors in an industrial data collectionenvironment. Embodiments include a system for data collection in anindustrial environment with self-organizing network coding for datatransport and a data structure supporting a haptic wearable interfacefor data presentation. Embodiments include a system for data collectionin an industrial environment with self-organizing network coding fordata transport and a data structure supporting a heat map interface fordata presentation. Embodiments include a system for data collection inan industrial environment with self-organizing network coding for datatransport and self-organized tuning of an interface layer for datapresentation.

As noted above, methods and systems are disclosed herein for a haptic ormulti-sensory user interface, including a wearable haptic ormulti-sensory user interface for an industrial sensor data collector,with vibration, heat, electrical, and/or sound outputs. Embodimentsinclude a wearable haptic user interface for conveying industrial stateinformation from a data collector, with vibration, heat, electrical,and/or sound outputs. Embodiments include a wearable haptic userinterface for conveying industrial state information from a datacollector, with vibration, heat, electrical, and/or sound outputs. Thewearable also has a visual presentation layer for presenting a heat mapthat indicates a parameter of the data. Embodiments includecondition-sensitive, self-organized tuning of AR/VR interfaces andmulti-sensory interfaces based on feedback metrics and/or training inindustrial environments.

As noted above, methods and systems are disclosed herein for apresentation layer for AR/VR industrial glasses, where heat map elementsare presented based on patterns and/or parameters in collected data.Embodiments include condition-sensitive, self-organized tuning of a heatmap AR/VR interface based on feedback metrics and/or training inindustrial environments. As noted above, methods and systems aredisclosed herein for condition-sensitive, self-organized tuning of AR/VRinterfaces based on feedback metrics and/or training in industrialenvironments.

The following illustrative clauses describe certain embodiments of thepresent disclosure. The data collection system mentioned in thefollowing disclosure may be a local data collection system 102, a hostprocessing system 112 (e.g., using a cloud platform), or a combinationof a local system and a host system. In embodiments, a data collectionsystem is provided having the use of an analog crosspoint switch forcollecting data having variable groups of analog sensor inputs. Inembodiments, a data collection and processing system is provided havingthe use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having IP front-end-endsignal conditioning on a multiplexer for improved signal-to-noise ratio.In embodiments, a data collection and processing system is providedhaving the use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having multiplexercontinuous monitoring alarming features. In embodiments, a datacollection and processing system is provided having the use of an analogcrosspoint switch for collecting data having variable groups of analogsensor inputs and having the use of distributed CPLD chips withdedicated bus for logic control of multiple MUX and data acquisitionsections. In embodiments, a data collection and processing system isprovided having the use of an analog crosspoint switch for collectingdata having variable groups of analog sensor inputs and havinghigh-amperage input capability using solid state relays and designtopology. In embodiments, a data collection and processing system isprovided having the use of an analog crosspoint switch for collectingdata having variable groups of analog sensor inputs and havingpower-down capability of at least one of an analog sensor channel and ofa component board. In embodiments, a data collection and processingsystem is provided having the use of an analog crosspoint switch forcollecting data having variable groups of analog sensor inputs andhaving unique electrostatic protection for trigger and vibration inputs.In embodiments, a data collection and processing system is providedhaving the use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having precise voltagereference for A/D zero reference.

In embodiments, a data collection and processing system is providedhaving the use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having a phase-lock loopband-pass tracking filter for obtaining slow-speed RPMs and phaseinformation. In embodiments, a data collection and processing system isprovided having the use of an analog crosspoint switch for collectingdata having variable groups of analog sensor inputs and having digitalderivation of phase relative to input and trigger channels usingon-board timers. In embodiments, a data collection and processing systemis provided having the use of an analog crosspoint switch for collectingdata having variable groups of analog sensor inputs and having apeak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection. In embodiments, a datacollection and processing system is provided having the use of an analogcrosspoint switch for collecting data having variable groups of analogsensor inputs and having the routing of a trigger channel that is eitherraw or buffered into other analog channels. In embodiments, a datacollection and processing system is provided having the use of an analogcrosspoint switch for collecting data having variable groups of analogsensor inputs and having the use of higher input oversampling fordelta-sigma A/D for lower sampling rate outputs to minimize AA filterrequirements. In embodiments, a data collection and processing system isprovided having the use of an analog crosspoint switch for collectingdata having variable groups of analog sensor inputs and having the useof a CPLD as a clock-divider for a delta-sigma analog-to-digitalconverter to achieve lower sampling rates without the need for digitalresampling.

In embodiments, a data collection and processing system is providedhaving the use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having long blocks of dataat a high-sampling rate, as opposed to multiple sets of data taken atdifferent sampling rates. In embodiments, a data collection andprocessing system is provided having the use of an analog crosspointswitch for collecting data having variable groups of analog sensorinputs and having storage of calibration data with maintenance historyon-board card set. In embodiments, a data collection and processingsystem is provided having the use of an analog crosspoint switch forcollecting data having variable groups of analog sensor inputs andhaving a rapid route creation capability using hierarchical templates.In embodiments, a data collection and processing system is providedhaving the use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having intelligentmanagement of data collection bands. In embodiments, a data collectionand processing system is provided having the use of an analog crosspointswitch for collecting data having variable groups of analog sensorinputs and having a neural net expert system using intelligentmanagement of data collection bands.

In embodiments, a data collection and processing system is providedhaving the use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having use of a databasehierarchy in sensor data analysis. In embodiments, a data collection andprocessing system is provided having the use of an analog crosspointswitch for collecting data having variable groups of analog sensorinputs and having an expert system GUI graphical approach to definingintelligent data collection bands and diagnoses for the expert system.In embodiments, a data collection and processing system is providedhaving the use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having a graphical approachfor back-calculation definition. In embodiments, a data collection andprocessing system is provided having the use of an analog crosspointswitch for collecting data having variable groups of analog sensorinputs and having proposed bearing analysis methods. In embodiments, adata collection and processing system is provided having the use of ananalog crosspoint switch for collecting data having variable groups ofanalog sensor inputs and having torsional vibration detection/analysisutilizing transitory signal analysis. In embodiments, a data collectionand processing system is provided having the use of an analog crosspointswitch for collecting data having variable groups of analog sensorinputs and having improved integration using both analog and digitalmethods.

In embodiments, a data collection and processing system is providedhaving the use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment. In embodiments, a data collection and processing system isprovided having the use of an analog crosspoint switch for collectingdata having variable groups of analog sensor inputs and having dataacquisition parking features. In embodiments, a data collection andprocessing system is provided having the use of an analog crosspointswitch for collecting data having variable groups of analog sensorinputs and having a self-sufficient data acquisition box. Inembodiments, a data collection and processing system is provided havingthe use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having SD card storage. Inembodiments, a data collection and processing system is provided havingthe use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having extended onboardstatistical capabilities for continuous monitoring. In embodiments, adata collection and processing system is provided having the use of ananalog crosspoint switch for collecting data having variable groups ofanalog sensor inputs and having the use of ambient, local and vibrationnoise for prediction. In embodiments, a data collection and processingsystem is provided having the use of an analog crosspoint switch forcollecting data having variable groups of analog sensor inputs andhaving smart route changes based on incoming data or alarms to enablesimultaneous dynamic data for analysis or correlation. In embodiments, adata collection and processing system is provided having the use of ananalog crosspoint switch for collecting data having variable groups ofanalog sensor inputs and having smart ODS and transfer functions. Inembodiments, a data collection and processing system is provided havingthe use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having a hierarchicalmultiplexer. In embodiments, a data collection and processing system isprovided having the use of an analog crosspoint switch for collectingdata having variable groups of analog sensor inputs and havingidentification of sensor overload. In embodiments, a data collection andprocessing system is provided having the use of an analog crosspointswitch for collecting data having variable groups of analog sensorinputs and having RF identification and an inclinometer.

In embodiments, a data collection and processing system is providedhaving the use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having continuous ultrasonicmonitoring. In embodiments, a data collection and processing system isprovided having the use of an analog crosspoint switch for collectingdata having variable groups of analog sensor inputs and havingcloud-based, machine pattern recognition based on the fusion of remote,analog industrial sensors. In embodiments, a data collection andprocessing system is provided having the use of an analog crosspointswitch for collecting data having variable groups of analog sensorinputs and having cloud-based, machine pattern analysis of stateinformation from multiple analog industrial sensors to provideanticipated state information for an industrial system. In embodiments,a data collection and processing system is provided having the use of ananalog crosspoint switch for collecting data having variable groups ofanalog sensor inputs and having cloud-based policy automation engine forIoT, with creation, deployment, and management of IoT devices. Inembodiments, a data collection and processing system is provided havingthe use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having on-device sensorfusion and data storage for industrial IoT devices. In embodiments, adata collection and processing system is provided having the use of ananalog crosspoint switch for collecting data having variable groups ofanalog sensor inputs and having a self-organizing data marketplace forindustrial IoT data. In embodiments, a data collection and processingsystem is provided having the use of an analog crosspoint switch forcollecting data having variable groups of analog sensor inputs andhaving self-organization of data pools based on utilization and/or yieldmetrics. In embodiments, a data collection and processing system isprovided having the use of an analog crosspoint switch for collectingdata having variable groups of analog sensor inputs and training AImodels based on industry-specific feedback. In embodiments, a datacollection and processing system is provided having the use of an analogcrosspoint switch for collecting data having variable groups of analogsensor inputs and having a self-organized swarm of industrial datacollectors. In embodiments, a data collection and processing system isprovided having the use of an analog crosspoint switch for collectingdata having variable groups of analog sensor inputs and having an IoTdistributed ledger. In embodiments, a data collection and processingsystem is provided having the use of an analog crosspoint switch forcollecting data having variable groups of analog sensor inputs andhaving a self-organizing collector. In embodiments, a data collectionand processing system is provided having the use of an analog crosspointswitch for collecting data having variable groups of analog sensorinputs and having a network-sensitive collector. In embodiments, a datacollection and processing system is provided having the use of an analogcrosspoint switch for collecting data having variable groups of analogsensor inputs and having a remotely organized collector. In embodiments,a data collection and processing system is provided having the use of ananalog crosspoint switch for collecting data having variable groups ofanalog sensor inputs and having a self-organizing storage for amulti-sensor data collector. In embodiments, a data collection andprocessing system is provided having the use of an analog crosspointswitch for collecting data having variable groups of analog sensorinputs and having a self-organizing network coding for multi-sensor datanetwork. In embodiments, a data collection and processing system isprovided having the use of an analog crosspoint switch for collectingdata having variable groups of analog sensor inputs and having awearable haptic user interface for an industrial sensor data collector,with vibration, heat, electrical, and/or sound outputs. In embodiments,a data collection and processing system is provided having the use of ananalog crosspoint switch for collecting data having variable groups ofanalog sensor inputs and having heat maps displaying collected data forAR/VR. In embodiments, a data collection and processing system isprovided having the use of an analog crosspoint switch for collectingdata having variable groups of analog sensor inputs and havingautomatically tuned AR/VR visualization of data collected by a datacollector.

In embodiments, a data collection and processing system is providedhaving IP front-end signal conditioning on a multiplexer for improvedsignal-to-noise ratio. In embodiments, a data collection and processingsystem is provided having IP front-end signal conditioning on amultiplexer for improved signal-to-noise ratio and having multiplexercontinuous monitoring alarming features. In embodiments, a datacollection and processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving the use of distributed CPLD chips with dedicated bus for logiccontrol of multiple MUX and data acquisition sections. In embodiments, adata collection and processing system is provided having IP front-endsignal conditioning on a multiplexer for improved signal-to-noise ratioand having high-amperage input capability using solid state relays anddesign topology. In embodiments, a data collection and processing systemis provided having IP front-end signal conditioning on a multiplexer forimproved signal-to-noise ratio and having power-down capability of atleast one analog sensor channel and of a component board. Inembodiments, a data collection and processing system is provided havingIP front-end signal conditioning on a multiplexer for improvedsignal-to-noise ratio and having unique electrostatic protection fortrigger and vibration inputs. In embodiments, a data collection andprocessing system is provided having IP front-end signal conditioning ona multiplexer for improved signal-to-noise ratio and having precisevoltage reference for A/D zero reference. In embodiments, a datacollection and processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving a phase-lock loop band-pass tracking filter for obtainingslow-speed RPMs and phase information. In embodiments, a data collectionand processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving digital derivation of phase relative to input and triggerchannels using on-board timers. In embodiments, a data collection andprocessing system is provided having IP front-end signal conditioning ona multiplexer for improved signal-to-noise ratio and having apeak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection. In embodiments, a datacollection and processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving routing of a trigger channel that is either raw or buffered intoother analog channels. In embodiments, a data collection and processingsystem is provided having IP front-end signal conditioning on amultiplexer for improved signal-to-noise ratio and having the use ofhigher input oversampling for delta-sigma A/D for lower sampling rateoutputs to minimize AA filter requirements. In embodiments, a datacollection and processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving the use of a CPLD as a clock-divider for a delta-sigmaanalog-to-digital converter to achieve lower sampling rates without theneed for digital resampling. In embodiments, a data collection andprocessing system is provided having IP front-end signal conditioning ona multiplexer for improved signal-to-noise ratio and having long blocksof data at a high-sampling rate as opposed to multiple sets of datataken at different sampling rates. In embodiments, a data collection andprocessing system is provided having IP front-end signal conditioning ona multiplexer for improved signal-to-noise ratio and having storage ofcalibration data with maintenance history on-board card set. Inembodiments, a data collection and processing system is provided havingIP front-end signal conditioning on a multiplexer for improvedsignal-to-noise ratio and having a rapid route creation capability usinghierarchical templates. In embodiments, a data collection and processingsystem is provided having IP front-end signal conditioning on amultiplexer for improved signal-to-noise ratio and having intelligentmanagement of data collection bands. In embodiments, a data collectionand processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving a neural net expert system using intelligent management of datacollection bands. In embodiments, a data collection and processingsystem is provided having IP front-end signal conditioning on amultiplexer for improved signal-to-noise ratio and having use of adatabase hierarchy in sensor data analysis. In embodiments, a datacollection and processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving an expert system GUI graphical approach to defining intelligentdata collection bands and diagnoses for the expert system. Inembodiments, a data collection and processing system is provided havingIP front-end signal conditioning on a multiplexer for improvedsignal-to-noise ratio and having a graphical approach forback-calculation definition. In embodiments, a data collection andprocessing system is provided having IP front-end signal conditioning ona multiplexer for improved signal-to-noise ratio and having proposedbearing analysis methods. In embodiments, a data collection andprocessing system is provided having IP front-end signal conditioning ona multiplexer for improved signal-to-noise ratio and having torsionalvibration detection/analysis utilizing transitory signal analysis. Inembodiments, a data collection and processing system is provided havingIP front-end signal conditioning on a multiplexer for improvedsignal-to-noise ratio and having improved integration using both analogand digital methods. In embodiments, a data collection and processingsystem is provided having IP front-end signal conditioning on amultiplexer for improved signal-to-noise ratio and having adaptivescheduling techniques for continuous monitoring of analog data in alocal environment. In embodiments, a data collection and processingsystem is provided having IP front-end signal conditioning on amultiplexer for improved signal-to-noise ratio and having dataacquisition parking features. In embodiments, a data collection andprocessing system is provided having IP front-end signal conditioning ona multiplexer for improved signal-to-noise ratio and having aself-sufficient data acquisition box. In embodiments, a data collectionand processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving SD card storage. In embodiments, a data collection and processingsystem is provided having IP front-end signal conditioning on amultiplexer for improved signal-to-noise ratio and having extendedonboard statistical capabilities for continuous monitoring. Inembodiments, a data collection and processing system is provided havingIP front-end signal conditioning on a multiplexer for improvedsignal-to-noise ratio and having the use of ambient, local and vibrationnoise for prediction. In embodiments, a data collection and processingsystem is provided having IP front-end signal conditioning on amultiplexer for improved signal-to-noise ratio and having smart routechanges route based on incoming data or alarms to enable simultaneousdynamic data for analysis or correlation. In embodiments, a datacollection and processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving smart ODS and transfer functions. In embodiments, a datacollection and processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving a hierarchical multiplexer. In embodiments, a data collection andprocessing system is provided having IP front-end signal conditioning ona multiplexer for improved signal-to-noise ratio and havingidentification of sensor overload. In embodiments, a data collection andprocessing system is provided having IP front-end signal conditioning ona multiplexer for improved signal-to-noise ratio and having RFidentification and an inclinometer. In embodiments, a data collectionand processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving continuous ultrasonic monitoring. In embodiments, a datacollection and processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving cloud-based, machine pattern recognition based on the fusion ofremote, analog industrial sensors. In embodiments, a data collection andprocessing system is provided having IP front-end signal conditioning ona multiplexer for improved signal-to-noise ratio and having cloud-based,machine pattern analysis of state information from multiple analogindustrial sensors to provide anticipated state information for anindustrial system. In embodiments, a data collection and processingsystem is provided having IP front-end signal conditioning on amultiplexer for improved signal-to-noise ratio and having cloud-basedpolicy automation engine for IoT, with creation, deployment, andmanagement of IoT devices. In embodiments, a data collection andprocessing system is provided having IP front-end signal conditioning ona multiplexer for improved signal-to-noise ratio and having on-devicesensor fusion and data storage for industrial IoT devices. Inembodiments, a data collection and processing system is provided havingIP front-end signal conditioning on a multiplexer for improvedsignal-to-noise ratio and having a self-organizing data marketplace forindustrial IoT data. In embodiments, a data collection and processingsystem is provided having IP front-end signal conditioning on amultiplexer for improved signal-to-noise ratio and havingself-organization of data pools based on utilization and/or yieldmetrics. In embodiments, a data collection and processing system isprovided having IP front-end signal conditioning on a multiplexer forimproved signal-to-noise ratio and having training AI models based onindustry-specific feedback. In embodiments, a data collection andprocessing system is provided having IP front-end signal conditioning ona multiplexer for improved signal-to-noise ratio and having aself-organized swarm of industrial data collectors. In embodiments, adata collection and processing system is provided having IP front-endsignal conditioning on a multiplexer for improved signal-to-noise ratioand having an IoT distributed ledger. In embodiments, a data collectionand processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving a self-organizing collector. In embodiments, a data collectionand processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving a network-sensitive collector. In embodiments, a data collectionand processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving a remotely organized collector. In embodiments, a data collectionand processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving a self-organizing storage for a multi-sensor data collector. Inembodiments, a data collection and processing system is provided havingIP front-end signal conditioning on a multiplexer for improvedsignal-to-noise ratio and having a self-organizing network coding formulti-sensor data network. In embodiments, a data collection andprocessing system is provided having IP front-end signal conditioning ona multiplexer for improved signal-to-noise ratio and having a wearablehaptic user interface for an industrial sensor data collector, withvibration, heat, electrical, and/or sound outputs. In embodiments, adata collection and processing system is provided having IP front-endsignal conditioning on a multiplexer for improved signal-to-noise ratioand having heat maps displaying collected data for AR/VR. Inembodiments, a data collection and processing system is provided havingIP front-end signal conditioning on a multiplexer for improvedsignal-to-noise ratio and having automatically tuned AR/VR visualizationof data collected by a data collector.

In embodiments, a data collection and processing system is providedhaving multiplexer continuous monitoring alarming features. Inembodiments, a data collection and processing system is provided havingmultiplexer continuous monitoring alarming features and having the useof distributed CPLD chips with dedicated bus for logic control ofmultiple MUX and data acquisition sections. In embodiments, a datacollection and processing system is provided having multiplexercontinuous monitoring alarming features and having high-amperage inputcapability using solid state relays and design topology. In embodiments,a data collection and processing system is provided having multiplexercontinuous monitoring alarming features and having power-down capabilityof at least one of an analog sensor channel and of a component board. Inembodiments, a data collection and processing system is provided havingmultiplexer continuous monitoring alarming features and having uniqueelectrostatic protection for trigger and vibration inputs. Inembodiments, a data collection and processing system is provided havingmultiplexer continuous monitoring alarming features and having precisevoltage reference for A/D zero reference. In embodiments, a datacollection and processing system is provided having multiplexercontinuous monitoring alarming features and having a phase-lock loopband-pass tracking filter for obtaining slow-speed RPMs and phaseinformation. In embodiments, a data collection and processing system isprovided having multiplexer continuous monitoring alarming features andhaving digital derivation of phase relative to input and triggerchannels using on-board timers. In embodiments, a data collection andprocessing system is provided having multiplexer continuous monitoringalarming features and having a peak-detector for auto-scaling that isrouted into a separate analog-to-digital converter for peak detection.In embodiments, a data collection and processing system is providedhaving multiplexer continuous monitoring alarming features and havingrouting of a trigger channel that is either raw or buffered into otheranalog channels. In embodiments, a data collection and processing systemis provided having multiplexer continuous monitoring alarming featuresand having the use of higher input oversampling for delta-sigma A/D forlower sampling rate outputs to minimize AA filter requirements. Inembodiments, a data collection and processing system is provided havingmultiplexer continuous monitoring alarming features and having the useof a CPLD as a clock-divider for a delta-sigma analog-to-digitalconverter to achieve lower sampling rates without the need for digitalresampling. In embodiments, a data collection and processing system isprovided having multiplexer continuous monitoring alarming features andhaving long blocks of data at a high-sampling rate as opposed tomultiple sets of data taken at different sampling rates. In embodiments,a data collection and processing system is provided having multiplexercontinuous monitoring alarming features and having storage ofcalibration data with maintenance history on-board card set. Inembodiments, a data collection and processing system is provided havingmultiplexer continuous monitoring alarming features and having a rapidroute creation capability using hierarchical templates. In embodiments,a data collection and processing system is provided having multiplexercontinuous monitoring alarming features and having intelligentmanagement of data collection bands. In embodiments, a data collectionand processing system is provided having multiplexer continuousmonitoring alarming features and having a neural net expert system usingintelligent management of data collection bands. In embodiments, a datacollection and processing system is provided having multiplexercontinuous monitoring alarming features and having use of a databasehierarchy in sensor data analysis. In embodiments, a data collection andprocessing system is provided having multiplexer continuous monitoringalarming features and having an expert system GUI graphical approach todefining intelligent data collection bands and diagnoses for the expertsystem. In embodiments, a data collection and processing system isprovided having multiplexer continuous monitoring alarming features andhaving a graphical approach for back-calculation definition. Inembodiments, a data collection and processing system is provided havingmultiplexer continuous monitoring alarming features and having proposedbearing analysis methods. In embodiments, a data collection andprocessing system is provided having multiplexer continuous monitoringalarming features and having torsional vibration detection/analysisutilizing transitory signal analysis. In embodiments, a data collectionand processing system is provided having multiplexer continuousmonitoring alarming features and having improved integration using bothanalog and digital methods. In embodiments, a data collection andprocessing system is provided having multiplexer continuous monitoringalarming features and having adaptive scheduling techniques forcontinuous monitoring of analog data in a local environment. Inembodiments, a data collection and processing system is provided havingmultiplexer continuous monitoring alarming features and having dataacquisition parking features. In embodiments, a data collection andprocessing system is provided having multiplexer continuous monitoringalarming features and having a self-sufficient data acquisition box. Inembodiments, a data collection and processing system is provided havingmultiplexer continuous monitoring alarming features and having SD cardstorage. In embodiments, a data collection and processing system isprovided having multiplexer continuous monitoring alarming features andhaving extended onboard statistical capabilities for continuousmonitoring. In embodiments, a data collection and processing system isprovided having multiplexer continuous monitoring alarming features andhaving the use of ambient, local and vibration noise for prediction. Inembodiments, a data collection and processing system is provided havingmultiplexer continuous monitoring alarming features and having smartroute changes route based on incoming data or alarms to enablesimultaneous dynamic data for analysis or correlation. In embodiments, adata collection and processing system is provided having multiplexercontinuous monitoring alarming features and having smart ODS andtransfer functions. In embodiments, a data collection and processingsystem is provided having multiplexer continuous monitoring alarmingfeatures and having a hierarchical multiplexer. In embodiments, a datacollection and processing system is provided having multiplexercontinuous monitoring alarming features and having identification ofsensor overload. In embodiments, a data collection and processing systemis provided having multiplexer continuous monitoring alarming features,and having RF identification, and an inclinometer. In embodiments, adata collection and processing system is provided having multiplexercontinuous monitoring alarming features and having continuous ultrasonicmonitoring. In embodiments, a data collection and processing system isprovided having multiplexer continuous monitoring alarming features andhaving cloud-based, machine pattern recognition based on the fusion ofremote, analog industrial sensors. In embodiments, a data collection andprocessing system is provided having multiplexer continuous monitoringalarming features and having cloud-based, machine pattern analysis ofstate information from multiple analog industrial sensors to provideanticipated state information for an industrial system. In embodiments,a data collection and processing system is provided having multiplexercontinuous monitoring alarming features and having cloud-based policyautomation engine for IoT, with creation, deployment, and management ofIoT devices. In embodiments, a data collection and processing system isprovided having multiplexer continuous monitoring alarming features andhaving on-device sensor fusion and data storage for industrial IoTdevices. In embodiments, a data collection and processing system isprovided having multiplexer continuous monitoring alarming features andhaving a self-organizing data marketplace for industrial IoT data. Inembodiments, a data collection and processing system is provided havingmultiplexer continuous monitoring alarming features and havingself-organization of data pools based on utilization and/or yieldmetrics. In embodiments, a data collection and processing system isprovided having multiplexer continuous monitoring alarming features andhaving training AI models based on industry-specific feedback. Inembodiments, a data collection and processing system is provided havingmultiplexer continuous monitoring alarming features and having aself-organized swarm of industrial data collectors. In embodiments, adata collection and processing system is provided having multiplexercontinuous monitoring alarming features and having an IoT distributedledger. In embodiments, a data collection and processing system isprovided having multiplexer continuous monitoring alarming features andhaving a self-organizing collector. In embodiments, a data collectionand processing system is provided having multiplexer continuousmonitoring alarming features and having a network-sensitive collector.In embodiments, a data collection and processing system is providedhaving multiplexer continuous monitoring alarming features and having aremotely organized collector. In embodiments, a data collection andprocessing system is provided having multiplexer continuous monitoringalarming features and having a self-organizing storage for amulti-sensor data collector. In embodiments, a data collection andprocessing system is provided having multiplexer continuous monitoringalarming features and having a self-organizing network coding formulti-sensor data network. In embodiments, a data collection andprocessing system is provided having multiplexer continuous monitoringalarming features and having a wearable haptic user interface for anindustrial sensor data collector, with vibration, heat, electrical,and/or sound outputs. In embodiments, a data collection and processingsystem is provided having multiplexer continuous monitoring alarmingfeatures and having heat maps displaying collected data for AR/VR. Inembodiments, a data collection and processing system is provided havingmultiplexer continuous monitoring alarming features and havingautomatically tuned AR/VR visualization of data collected by a datacollector.

In embodiments, a data collection and processing system is providedhaving the use of distributed CPLD chips with dedicated bus for logiccontrol of multiple MUX and data acquisition sections. In embodiments, adata collection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having high-amperage inputcapability using solid state relays and design topology. In embodiments,a data collection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having power-down capability of atleast one of an analog sensor channel and of a component board. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having uniqueelectrostatic protection for trigger and vibration inputs. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having precise voltagereference for A/D zero reference. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having a phase-lock loop band-pass trackingfilter for obtaining slow-speed RPMs and phase information. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having digitalderivation of phase relative to input and trigger channels usingon-board timers. In embodiments, a data collection and processing systemis provided having the use of distributed CPLD chips with dedicated busfor logic control of multiple MUX and data acquisition sections andhaving a peak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection. In embodiments, a datacollection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having routing of a triggerchannel that is either raw or buffered into other analog channels. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having the use ofhigher input oversampling for delta-sigma A/D for lower sampling rateoutputs to minimize AA filter requirements. In embodiments, a datacollection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having long blocks ofdata at a high-sampling rate as opposed to multiple sets of data takenat different sampling rates. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having storage of calibration data withmaintenance history on-board card set. In embodiments, a data collectionand processing system is provided having the use of distributed CPLDchips with dedicated bus for logic control of multiple MUX and dataacquisition sections and having a rapid route creation capability usinghierarchical templates. In embodiments, a data collection and processingsystem is provided having the use of distributed CPLD chips withdedicated bus for logic control of multiple MUX and data acquisitionsections and having intelligent management of data collection bands. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having a neural netexpert system using intelligent management of data collection bands. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having use of adatabase hierarchy in sensor data analysis. In embodiments, a datacollection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having a graphical approach forback-calculation definition. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having proposed bearing analysis methods. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having torsionalvibration detection/analysis utilizing transitory signal analysis. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having improvedintegration using both analog and digital methods. In embodiments, adata collection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment. In embodiments, a data collection and processing system isprovided having the use of distributed CPLD chips with dedicated bus forlogic control of multiple MUX and data acquisition sections and havingdata acquisition parking features. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having a self-sufficient data acquisition box.In embodiments, a data collection and processing system is providedhaving the use of distributed CPLD chips with dedicated bus for logiccontrol of multiple MUX and data acquisition sections and having SD cardstorage. In embodiments, a data collection and processing system isprovided having the use of distributed CPLD chips with dedicated bus forlogic control of multiple MUX and data acquisition sections and havingextended onboard statistical capabilities for continuous monitoring. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having the use ofambient, local and vibration noise for prediction. In embodiments, adata collection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having smart route changes routebased on incoming data or alarms to enable simultaneous dynamic data foranalysis or correlation. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having smart ODS and transfer functions. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having a hierarchicalmultiplexer. In embodiments, a data collection and processing system isprovided having the use of distributed CPLD chips with dedicated bus forlogic control of multiple MUX and data acquisition sections and havingidentification of sensor overload. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having RF identification and an inclinometer.In embodiments, a data collection and processing system is providedhaving the use of distributed CPLD chips with dedicated bus for logiccontrol of multiple MUX and data acquisition sections and havingcontinuous ultrasonic monitoring. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having cloud-based, machine pattern recognitionbased on fusion of remote, analog industrial sensors. In embodiments, adata collection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having cloud-based, machinepattern analysis of state information from multiple analog industrialsensors to provide anticipated state information for an industrialsystem. In embodiments, a data collection and processing system isprovided having the use of distributed CPLD chips with dedicated bus forlogic control of multiple MUX and data acquisition sections and havingcloud-based policy automation engine for IoT, with creation, deployment,and management of IoT devices. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having on-device sensor fusion and data storagefor industrial IoT devices. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having a self-organizing data marketplace forindustrial IoT data. In embodiments, a data collection and processingsystem is provided having the use of distributed CPLD chips withdedicated bus for logic control of multiple MUX and data acquisitionsections and having self-organization of data pools based on utilizationand/or yield metrics. In embodiments, a data collection and processingsystem is provided having the use of distributed CPLD chips withdedicated bus for logic control of multiple MUX and data acquisitionsections and having training AI models based on industry-specificfeedback. In embodiments, a data collection and processing system isprovided having the use of distributed CPLD chips with dedicated bus forlogic control of multiple MUX and data acquisition sections and having aself-organized swarm of industrial data collectors. In embodiments, adata collection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having an IoT distributed ledger.In embodiments, a data collection and processing system is providedhaving the use of distributed CPLD chips with dedicated bus for logiccontrol of multiple MUX and data acquisition sections and having aself-organizing collector. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having a network-sensitive collector. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having a remotelyorganized collector. In embodiments, a data collection and processingsystem is provided having the use of distributed CPLD chips withdedicated bus for logic control of multiple MUX and data acquisitionsections and having a self-organizing storage for a multi-sensor datacollector. In embodiments, a data collection and processing system isprovided having the use of distributed CPLD chips with dedicated bus forlogic control of multiple MUX and data acquisition sections and having aself-organizing network coding for multi-sensor data network. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having a wearablehaptic user interface for an industrial sensor data collector, withvibration, heat, electrical and/or sound outputs. In embodiments, a datacollection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having heat maps displayingcollected data for AR/VR. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having automatically tuned AR/VR visualizationof data collected by a data collector.

In embodiments, a data collection and processing system is providedhaving high-amperage input capability using solid state relays anddesign topology. In embodiments, a data collection and processing systemis provided having high-amperage input capability using solid staterelays and design topology and having power-down capability of at leastone of an analog sensor channel and of a component board. Inembodiments, a data collection and processing system is provided havinghigh-amperage input capability using solid state relays and designtopology and having unique electrostatic protection for trigger andvibration inputs. In embodiments, a data collection and processingsystem is provided having high-amperage input capability using solidstate relays and design topology and having precise voltage referencefor A/D zero reference. In embodiments, a data collection and processingsystem is provided having high-amperage input capability using solidstate relays and design topology and having a phase-lock loop band-passtracking filter for obtaining slow-speed RPMs and phase information. Inembodiments, a data collection and processing system is provided havinghigh-amperage input capability using solid state relays and designtopology and having digital derivation of phase relative to input andtrigger channels using on-board timers. In embodiments, a datacollection and processing system is provided having high-amperage inputcapability using solid state relays and design topology and having apeak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection. In embodiments, a datacollection and processing system is provided having high-amperage inputcapability using solid state relays and design topology and havingrouting of a trigger channel that is either raw or buffered into otheranalog channels. In embodiments, a data collection and processing systemis provided having high-amperage input capability using solid staterelays and design topology and having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements. In embodiments, a data collection andprocessing system is provided having high-amperage input capabilityusing solid state relays and design topology and having the use of aCPLD as a clock-divider for a delta-sigma analog-to-digital converter toachieve lower sampling rates without the need for digital resampling. Inembodiments, a data collection and processing system is provided havinghigh-amperage input capability using solid state relays and designtopology and having long blocks of data at a high-sampling rate asopposed to multiple sets of data taken at different sampling rates. Inembodiments, a data collection and processing system is provided havinghigh-amperage input capability using solid state relays and designtopology and having storage of calibration data with maintenance historyon-board card set. In embodiments, a data collection and processingsystem is provided having high-amperage input capability using solidstate relays and design topology and having a rapid route creationcapability using hierarchical templates. In embodiments, a datacollection and processing system is provided having high-amperage inputcapability using solid state relays and design topology and havingintelligent management of data collection bands. In embodiments, a datacollection and processing system is provided having high-amperage inputcapability using solid state relays and design topology and having aneural net expert system using intelligent management of data collectionbands. In embodiments, a data collection and processing system isprovided having high-amperage input capability using solid state relaysand design topology and having use of a database hierarchy in sensordata analysis. In embodiments, a data collection and processing systemis provided having high-amperage input capability using solid staterelays and design topology and having an expert system GUI graphicalapproach to defining intelligent data collection bands and diagnoses forthe expert system. In embodiments, a data collection and processingsystem is provided having high-amperage input capability using solidstate relays and design topology and having a graphical approach forback-calculation definition. In embodiments, a data collection andprocessing system is provided having high-amperage input capabilityusing solid state relays and design topology and having proposed bearinganalysis methods. In embodiments, a data collection and processingsystem is provided having high-amperage input capability using solidstate relays and design topology and having torsional vibrationdetection/analysis utilizing transitory signal analysis. In embodiments,a data collection and processing system is provided having high-amperageinput capability using solid state relays and design topology and havingimproved integration using both analog and digital methods. Inembodiments, a data collection and processing system is provided havinghigh-amperage input capability using solid state relays and designtopology and having adaptive scheduling techniques for continuousmonitoring of analog data in a local environment. In embodiments, a datacollection and processing system is provided having high-amperage inputcapability using solid state relays and design topology and having dataacquisition parking features. In embodiments, a data collection andprocessing system is provided having high-amperage input capabilityusing solid state relays and design topology and having aself-sufficient data acquisition box. In embodiments, a data collectionand processing system is provided having high-amperage input capabilityusing solid state relays and design topology and having SD card storage.In embodiments, a data collection and processing system is providedhaving high-amperage input capability using solid state relays anddesign topology and having extended onboard statistical capabilities forcontinuous monitoring. In embodiments, a data collection and processingsystem is provided having high-amperage input capability using solidstate relays and design topology and having the use of ambient, localand vibration noise for prediction. In embodiments, a data collectionand processing system is provided having high-amperage input capabilityusing solid state relays and design topology and having smart routechanges route based on incoming data or alarms to enable simultaneousdynamic data for analysis or correlation. In embodiments, a datacollection and processing system is provided having high-amperage inputcapability using solid state relays and design topology and having smartODS and transfer functions. In embodiments, a data collection andprocessing system is provided having high-amperage input capabilityusing solid state relays and design topology and having a hierarchicalmultiplexer. In embodiments, a data collection and processing system isprovided having high-amperage input capability using solid state relaysand design topology and having identification of sensor overload. Inembodiments, a data collection and processing system is provided havinghigh-amperage input capability using solid state relays and designtopology and having RF identification and an inclinometer. Inembodiments, a data collection and processing system is provided havinghigh-amperage input capability using solid state relays and designtopology and having continuous ultrasonic monitoring. In embodiments, adata collection and processing system is provided having high-amperageinput capability using solid state relays and design topology and havingcloud-based, machine pattern recognition based on fusion of remote,analog industrial sensors. In embodiments, a data collection andprocessing system is provided having high-amperage input capabilityusing solid state relays and design topology and having cloud-based,machine pattern analysis of state information from multiple analogindustrial sensors to provide anticipated state information for anindustrial system. In embodiments, a data collection and processingsystem is provided having high-amperage input capability using solidstate relays and design topology and having cloud-based policyautomation engine for IoT, with creation, deployment, and management ofIoT devices. In embodiments, a data collection and processing system isprovided having high-amperage input capability using solid state relaysand design topology and having on-device sensor fusion and data storagefor industrial IoT devices. In embodiments, a data collection andprocessing system is provided having high-amperage input capabilityusing solid state relays and design topology and having aself-organizing data marketplace for industrial IoT data. Inembodiments, a data collection and processing system is provided havinghigh-amperage input capability using solid state relays and designtopology and having self-organization of data pools based on utilizationand/or yield metrics. In embodiments, a data collection and processingsystem is provided having high-amperage input capability using solidstate relays and design topology and having training AI models based onindustry-specific feedback. In embodiments, a data collection andprocessing system is provided having high-amperage input capabilityusing solid state relays and design topology and having a self-organizedswarm of industrial data collectors. In embodiments, a data collectionand processing system is provided having high-amperage input capabilityusing solid state relays and design topology and having an IoTdistributed ledger. In embodiments, a data collection and processingsystem is provided having high-amperage input capability using solidstate relays and design topology and having a self-organizing collector.In embodiments, a data collection and processing system is providedhaving high-amperage input capability using solid state relays anddesign topology and having a network-sensitive collector. Inembodiments, a data collection and processing system is provided havinghigh-amperage input capability using solid state relays and designtopology and having a remotely organized collector. In embodiments, adata collection and processing system is provided having high-amperageinput capability using solid state relays and design topology and havinga self-organizing storage for a multi-sensor data collector. Inembodiments, a data collection and processing system is provided havinghigh-amperage input capability using solid state relays and designtopology and having a self-organizing network coding for multi-sensordata network. In embodiments, a data collection and processing system isprovided having high-amperage input capability using solid state relaysand design topology and having a wearable haptic user interface for anindustrial sensor data collector, with vibration, heat, electrical,and/or sound outputs. In embodiments, a data collection and processingsystem is provided having high-amperage input capability using solidstate relays and design topology and having heat maps displayingcollected data for AR/VR. In embodiments, a data collection andprocessing system is provided having high-amperage input capabilityusing solid state relays and design topology and having automaticallytuned AR/VR visualization of data collected by a data collector.

In embodiments, a data collection and processing system is providedhaving power-down capability for at least one of an analog sensor and acomponent board. In embodiments, a data collection and processing systemis provided having power-down capability for at least one of an analogsensor and a component board and having unique electrostatic protectionfor trigger and vibration inputs. In embodiments, a data collection andprocessing system is provided having power-down capability for at leastone of an analog sensor and a component board and having precise voltagereference for A/D zero reference. In embodiments, a data collection andprocessing system is provided having power-down capability for at leastone of an analog sensor and a component board and having a phase-lockloop band-pass tracking filter for obtaining slow-speed RPMs and phaseinformation. In embodiments, a data collection and processing system isprovided having power-down capability for at least one of an analogsensor and a component board and having digital derivation of phaserelative to input and trigger channels using on-board timers. Inembodiments, a data collection and processing system is provided havingpower-down capability for at least one of an analog sensor and acomponent board and having a peak-detector for auto-scaling that isrouted into a separate analog-to-digital converter for peak detection.In embodiments, a data collection and processing system is providedhaving power-down capability for at least one of an analog sensor and acomponent board and having routing of a trigger channel that is eitherraw or buffered into other analog channels. In embodiments, a datacollection and processing system is provided having power-downcapability for at least one of an analog sensor and a component boardand having the use of higher input oversampling for delta-sigma A/D forlower sampling rate outputs to minimize AA filter requirements. Inembodiments, a data collection and processing system is provided havingpower-down capability for at least one of an analog sensor and acomponent board and having the use of a CPLD as a clock-divider for adelta-sigma analog-to-digital converter to achieve lower sampling rateswithout the need for digital resampling. In embodiments, a datacollection and processing system is provided having power-downcapability for at least one of an analog sensor and a component boardand having long blocks of data at a high-sampling rate as opposed tomultiple sets of data taken at different sampling rates. In embodiments,a data collection and processing system is provided having power-downcapability for at least one of an analog sensor and a component boardand having storage of calibration data with maintenance history on-boardcard set. In embodiments, a data collection and processing system isprovided having power-down capability for at least one of an analogsensor and a component board and having a rapid route creationcapability using hierarchical templates. In embodiments, a datacollection and processing system is provided having power-downcapability for at least one of an analog sensor and a component boardand having intelligent management of data collection bands. Inembodiments, a data collection and processing system is provided havingpower-down capability for at least one of an analog sensor and acomponent board and having a neural net expert system using intelligentmanagement of data collection bands. In embodiments, a data collectionand processing system is provided having power-down capability for atleast one of an analog sensor and a component board and having use of adatabase hierarchy in sensor data analysis. In embodiments, a datacollection and processing system is provided having power-downcapability for at least one of an analog sensor and a component boardand having an expert system GUI graphical approach to definingintelligent data collection bands and diagnoses for the expert system.In embodiments, a data collection and processing system is providedhaving power-down capability for at least one of an analog sensor and acomponent board and having a graphical approach for back-calculationdefinition. In embodiments, a data collection and processing system isprovided having power-down capability for at least one of an analogsensor and a component board and having proposed bearing analysismethods. In embodiments, a data collection and processing system isprovided having power-down capability for at least one of an analogsensor and a component board and having torsional vibrationdetection/analysis utilizing transitory signal analysis. In embodiments,a data collection and processing system is provided having power-downcapability for at least one of an analog sensor and a component boardand having improved integration using both analog and digital methods.In embodiments, a data collection and processing system is providedhaving power-down capability for at least one of an analog sensor and acomponent board and having adaptive scheduling techniques for continuousmonitoring of analog data in a local environment. In embodiments, a datacollection and processing system is provided having power-downcapability for at least one of an analog sensor and a component boardand having data acquisition parking features. In embodiments, a datacollection and processing system is provided having power-downcapability for at least one of an analog sensor and a component boardand having a self-sufficient data acquisition box. In embodiments, adata collection and processing system is provided having power-downcapability for at least one of an analog sensor and a component boardand having SD card storage. In embodiments, a data collection andprocessing system is provided having power-down capability for at leastone of an analog sensor and a component board and having extendedonboard statistical capabilities for continuous monitoring. Inembodiments, a data collection and processing system is provided havingpower-down capability for at least one of an analog sensor and acomponent board and having the use of ambient, local and vibration noisefor prediction. In embodiments, a data collection and processing systemis provided having power-down capability for at least one of an analogsensor and a component board and having smart route changes route basedon incoming data or alarms to enable simultaneous dynamic data foranalysis or correlation. In embodiments, a data collection andprocessing system is provided having power-down capability for at leastone of an analog sensor and a component board and having smart ODS andtransfer functions. In embodiments, a data collection and processingsystem is provided having power-down capability for at least one of ananalog sensor and a component board and having a hierarchicalmultiplexer. In embodiments, a data collection and processing system isprovided having power-down capability for at least one of an analogsensor and a component board and having identification of sensoroverload. In embodiments, a data collection and processing system isprovided having power-down capability for at least one of an analogsensor and a component board and having RF identification and aninclinometer. In embodiments, a data collection and processing system isprovided having power-down capability for at least one of an analogsensor and a component board and having continuous ultrasonicmonitoring. In embodiments, a data collection and processing system isprovided having power-down capability for at least one of an analogsensor and a component board and having cloud-based, machine patternrecognition based on fusion of remote, analog industrial sensors. Inembodiments, a data collection and processing system is provided havingpower-down capability for at least one of an analog sensor and acomponent board and having cloud-based, machine pattern analysis ofstate information from multiple analog industrial sensors to provideanticipated state information for an industrial system. In embodiments,a data collection and processing system is provided having power-downcapability for at least one of an analog sensor and a component boardand having cloud-based policy automation engine for IoT, with creation,deployment, and management of IoT devices. In embodiments, a datacollection and processing system is provided having power-downcapability for at least one of an analog sensor and a component boardand having on-device sensor fusion and data storage for industrial IoTdevices. In embodiments, a data collection and processing system isprovided having power-down capability for at least one of an analogsensor and a component board and having a self-organizing datamarketplace for industrial IoT data. In embodiments, a data collectionand processing system is provided having power-down capability for atleast one of an analog sensor and a component board and havingself-organization of data pools based on utilization and/or yieldmetrics. In embodiments, a data collection and processing system isprovided having power-down capability for at least one of an analogsensor and a component board and having training AI models based onindustry-specific feedback. In embodiments, a data collection andprocessing system is provided having power-down capability for at leastone of an analog sensor and a component board and having aself-organized swarm of industrial data collectors. In embodiments, adata collection and processing system is provided having power-downcapability for at least one of an analog sensor and a component boardand having an IoT distributed ledger. In embodiments, a data collectionand processing system is provided having power-down capability for atleast one of an analog sensor and a component board and having aself-organizing collector. In embodiments, a data collection andprocessing system is provided having power-down capability for at leastone of an analog sensor and a component board and having anetwork-sensitive collector. In embodiments, a data collection andprocessing system is provided having power-down capability for at leastone of an analog sensor and a component board and having a remotelyorganized collector. In embodiments, a data collection and processingsystem is provided having power-down capability for at least one of ananalog sensor and a component board and having a self-organizing storagefor a multi-sensor data collector. In embodiments, a data collection andprocessing system is provided having power-down capability for at leastone of an analog sensor and a component board and having aself-organizing network coding for multi-sensor data network. Inembodiments, a data collection and processing system is provided havingpower-down capability for at least one of an analog sensor and acomponent board and having a wearable haptic user interface for anindustrial sensor data collector, with vibration, heat, electricaland/or sound outputs. In embodiments, a data collection and processingsystem is provided having power-down capability for at least one of ananalog sensor and a component board and having heat maps displayingcollected data for AR/VR. In embodiments, a data collection andprocessing system is provided having power-down capability for at leastone of an analog sensor and a component board and having automaticallytuned AR/VR visualization of data collected by a data collector.

In embodiments, a data collection and processing system is providedhaving unique electrostatic protection for trigger and vibration inputs.In embodiments, a data collection and processing system is providedhaving unique electrostatic protection for trigger and vibration inputsand having precise voltage reference for A/D zero reference. Inembodiments, a data collection and processing system is provided havingunique electrostatic protection for trigger and vibration inputs andhaving a phase-lock loop band-pass tracking filter for obtainingslow-speed RPMs and phase information. In embodiments, a data collectionand processing system is provided having unique electrostatic protectionfor trigger and vibration inputs and having digital derivation of phaserelative to input and trigger channels using on-board timers. Inembodiments, a data collection and processing system is provided havingunique electrostatic protection for trigger and vibration inputs andhaving a peak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection. In embodiments, a datacollection and processing system is provided having unique electrostaticprotection for trigger and vibration inputs and having routing of atrigger channel that is either raw or buffered into other analogchannels. In embodiments, a data collection and processing system isprovided having unique electrostatic protection for trigger andvibration inputs and having the use of higher input oversampling fordelta-sigma A/D for lower sampling rate outputs to minimize AA filterrequirements. In embodiments, a data collection and processing system isprovided having unique electrostatic protection for trigger andvibration inputs and having the use of a CPLD as a clock-divider for adelta-sigma analog-to-digital converter to achieve lower sampling rateswithout the need for digital resampling. In embodiments, a datacollection and processing system is provided having unique electrostaticprotection for trigger and vibration inputs and having long blocks ofdata at a high-sampling rate as opposed to multiple sets of data takenat different sampling rates. In embodiments, a data collection andprocessing system is provided having unique electrostatic protection fortrigger and vibration inputs and having storage of calibration data withmaintenance history on-board card set. In embodiments, a data collectionand processing system is provided having unique electrostatic protectionfor trigger and vibration inputs and having a rapid route creationcapability using hierarchical templates. In embodiments, a datacollection and processing system is provided having unique electrostaticprotection for trigger and vibration inputs and having intelligentmanagement of data collection bands. In embodiments, a data collectionand processing system is provided having unique electrostatic protectionfor trigger and vibration inputs and having a neural net expert systemusing intelligent management of data collection bands. In embodiments, adata collection and processing system is provided having uniqueelectrostatic protection for trigger and vibration inputs and having useof a database hierarchy in sensor data analysis. In embodiments, a datacollection and processing system is provided having unique electrostaticprotection for trigger and vibration inputs and having an expert systemGUI graphical approach to defining intelligent data collection bands anddiagnoses for the expert system. In embodiments, a data collection andprocessing system is provided having unique electrostatic protection fortrigger and vibration inputs and having a graphical approach forback-calculation definition. In embodiments, a data collection andprocessing system is provided having unique electrostatic protection fortrigger and vibration inputs and having proposed bearing analysismethods. In embodiments, a data collection and processing system isprovided having unique electrostatic protection for trigger andvibration inputs and having torsional vibration detection/analysisutilizing transitory signal analysis. In embodiments, a data collectionand processing system is provided having unique electrostatic protectionfor trigger and vibration inputs and having improved integration usingboth analog and digital methods. In embodiments, a data collection andprocessing system is provided having unique electrostatic protection fortrigger and vibration inputs and having adaptive scheduling techniquesfor continuous monitoring of analog data in a local environment. Inembodiments, a data collection and processing system is provided havingunique electrostatic protection for trigger and vibration inputs andhaving data acquisition parking features. In embodiments, a datacollection and processing system is provided having unique electrostaticprotection for trigger and vibration inputs and having a self-sufficientdata acquisition box. In embodiments, a data collection and processingsystem is provided having unique electrostatic protection for triggerand vibration inputs and having SD card storage. In embodiments, a datacollection and processing system is provided having unique electrostaticprotection for trigger and vibration inputs and having extended onboardstatistical capabilities for continuous monitoring. In embodiments, adata collection and processing system is provided having uniqueelectrostatic protection for trigger and vibration inputs and having theuse of ambient, local and vibration noise for prediction. Inembodiments, a data collection and processing system is provided havingunique electrostatic protection for trigger and vibration inputs andhaving smart route changes route based on incoming data or alarms toenable simultaneous dynamic data for analysis or correlation. Inembodiments, a data collection and processing system is provided havingunique electrostatic protection for trigger and vibration inputs andhaving smart ODS and transfer functions. In embodiments, a datacollection and processing system is provided having unique electrostaticprotection for trigger and vibration inputs and having a hierarchicalmultiplexer. In embodiments, a data collection and processing system isprovided having unique electrostatic protection for trigger andvibration inputs and having identification of sensor overload. Inembodiments, a data collection and processing system is provided havingunique electrostatic protection for trigger and vibration inputs andhaving RF identification and an inclinometer. In embodiments, a datacollection and processing system is provided having unique electrostaticprotection for trigger and vibration inputs and having continuousultrasonic monitoring. In embodiments, a data collection and processingsystem is provided having unique electrostatic protection for triggerand vibration inputs and having cloud-based, machine pattern recognitionbased on fusion of remote, analog industrial sensors. In embodiments, adata collection and processing system is provided having uniqueelectrostatic protection for trigger and vibration inputs and havingcloud-based, machine pattern analysis of state information from multipleanalog industrial sensors to provide anticipated state information foran industrial system. In embodiments, a data collection and processingsystem is provided having unique electrostatic protection for triggerand vibration inputs and having cloud-based policy automation engine forIoT, with creation, deployment, and management of IoT devices. Inembodiments, a data collection and processing system is provided havingunique electrostatic protection for trigger and vibration inputs andhaving on-device sensor fusion and data storage for industrial IoTdevices. In embodiments, a data collection and processing system isprovided having unique electrostatic protection for trigger andvibration inputs and having a self-organizing data marketplace forindustrial IoT data. In embodiments, a data collection and processingsystem is provided having unique electrostatic protection for triggerand vibration inputs and having self-organization of data pools based onutilization and/or yield metrics. In embodiments, a data collection andprocessing system is provided having unique electrostatic protection fortrigger and vibration inputs and having training AI models based onindustry-specific feedback. In embodiments, a data collection andprocessing system is provided having unique electrostatic protection fortrigger and vibration inputs and having a self-organized swarm ofindustrial data collectors. In embodiments, a data collection andprocessing system is provided having unique electrostatic protection fortrigger and vibration inputs and having an IoT distributed ledger. Inembodiments, a data collection and processing system is provided havingunique electrostatic protection for trigger and vibration inputs andhaving a self-organizing collector. In embodiments, a data collectionand processing system is provided having unique electrostatic protectionfor trigger and vibration inputs and having a network-sensitivecollector. In embodiments, a data collection and processing system isprovided having unique electrostatic protection for trigger andvibration inputs and having a remotely organized collector. Inembodiments, a data collection and processing system is provided havingunique electrostatic protection for trigger and vibration inputs andhaving a self-organizing storage for a multi-sensor data collector. Inembodiments, a data collection and processing system is provided havingunique electrostatic protection for trigger and vibration inputs andhaving a self-organizing network coding for multi-sensor data network.In embodiments, a data collection and processing system is providedhaving unique electrostatic protection for trigger and vibration inputsand having a wearable haptic user interface for an industrial sensordata collector, with vibration, heat, electrical and/or sound outputs.In embodiments, a data collection and processing system is providedhaving unique electrostatic protection for trigger and vibration inputsand having heat maps displaying collected data for AR/VR. Inembodiments, a data collection and processing system is provided havingunique electrostatic protection for trigger and vibration inputs andhaving automatically tuned AR/VR visualization of data collected by adata collector.

In embodiments, a data collection and processing system is providedhaving precise voltage reference for A/D zero reference. In embodiments,a data collection and processing system is provided having precisevoltage reference for A/D zero reference and having a phase-lock loopband-pass tracking filter for obtaining slow-speed RPMs and phaseinformation. In embodiments, a data collection and processing system isprovided having precise voltage reference for A/D zero reference andhaving digital derivation of phase relative to input and triggerchannels using on-board timers. In embodiments, a data collection andprocessing system is provided having precise voltage reference for A/Dzero reference and having a peak-detector for auto-scaling that isrouted into a separate analog-to-digital converter for peak detection.In embodiments, a data collection and processing system is providedhaving precise voltage reference for A/D zero reference and havingrouting of a trigger channel that is either raw or buffered into otheranalog channels. In embodiments, a data collection and processing systemis provided having precise voltage reference for A/D zero reference andhaving the use of higher input oversampling for delta-sigma A/D forlower sampling rate outputs to minimize AA filter requirements. Inembodiments, a data collection and processing system is provided havingprecise voltage reference for A/D zero reference and having the use of aCPLD as a clock-divider for a delta-sigma analog-to-digital converter toachieve lower sampling rates without the need for digital resampling. Inembodiments, a data collection and processing system is provided havingprecise voltage reference for A/D zero reference and having long blocksof data at a high-sampling rate as opposed to multiple sets of datataken at different sampling rates. In embodiments, a data collection andprocessing system is provided having precise voltage reference for A/Dzero reference and having storage of calibration data with maintenancehistory on-board card set. In embodiments, a data collection andprocessing system is provided having precise voltage reference for A/Dzero reference and having a rapid route creation capability usinghierarchical templates. In embodiments, a data collection and processingsystem is provided having precise voltage reference for A/D zeroreference and having intelligent management of data collection bands. Inembodiments, a data collection and processing system is provided havingprecise voltage reference for A/D zero reference and having a neural netexpert system using intelligent management of data collection bands. Inembodiments, a data collection and processing system is provided havingprecise voltage reference for A/D zero reference and having use of adatabase hierarchy in sensor data analysis. In embodiments, a datacollection and processing system is provided having precise voltagereference for A/D zero reference and having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system. In embodiments, a data collection andprocessing system is provided having precise voltage reference for A/Dzero reference and having a graphical approach for back-calculationdefinition. In embodiments, a data collection and processing system isprovided having precise voltage reference for A/D zero reference andhaving proposed bearing analysis methods. In embodiments, a datacollection and processing system is provided having precise voltagereference for A/D zero reference and having torsional vibrationdetection/analysis utilizing transitory signal analysis. In embodiments,a data collection and processing system is provided having precisevoltage reference for A/D zero reference and having improved integrationusing both analog and digital methods. In embodiments, a data collectionand processing system is provided having precise voltage reference forA/D zero reference and having adaptive scheduling techniques forcontinuous monitoring of analog data in a local environment. Inembodiments, a data collection and processing system is provided havingprecise voltage reference for A/D zero reference and having dataacquisition parking features. In embodiments, a data collection andprocessing system is provided having precise voltage reference for A/Dzero reference and having a self-sufficient data acquisition box. Inembodiments, a data collection and processing system is provided havingprecise voltage reference for A/D zero reference and having SD cardstorage. In embodiments, a data collection and processing system isprovided having precise voltage reference for A/D zero reference andhaving extended onboard statistical capabilities for continuousmonitoring. In embodiments, a data collection and processing system isprovided having precise voltage reference for A/D zero reference andhaving the use of ambient, local and vibration noise for prediction. Inembodiments, a data collection and processing system is provided havingprecise voltage reference for A/D zero reference and having smart routechanges route based on incoming data or alarms to enable simultaneousdynamic data for analysis or correlation. In embodiments, a datacollection and processing system is provided having precise voltagereference for A/D zero reference and having smart ODS and transferfunctions. In embodiments, a data collection and processing system isprovided having precise voltage reference for A/D zero reference andhaving a hierarchical multiplexer. In embodiments, a data collection andprocessing system is provided having precise voltage reference for A/Dzero reference and having identification of sensor overload. Inembodiments, a data collection and processing system is provided havingprecise voltage reference for A/D zero reference and having RFidentification and an inclinometer. In embodiments, a data collectionand processing system is provided having precise voltage reference forA/D zero reference and having continuous ultrasonic monitoring. Inembodiments, a data collection and processing system is provided havingprecise voltage reference for A/D zero reference and having cloud-based,machine pattern recognition based on fusion of remote, analog industrialsensors. In embodiments, a data collection and processing system isprovided having precise voltage reference for A/D zero reference andhaving cloud-based, machine pattern analysis of state information frommultiple analog industrial sensors to provide anticipated stateinformation for an industrial system. In embodiments, a data collectionand processing system is provided having precise voltage reference forA/D zero reference and having cloud-based policy automation engine forIoT, with creation, deployment, and management of IoT devices. Inembodiments, a data collection and processing system is provided havingprecise voltage reference for A/D zero reference and having on-devicesensor fusion and data storage for industrial IoT devices. Inembodiments, a data collection and processing system is provided havingprecise voltage reference for A/D zero reference and having aself-organizing data marketplace for industrial IoT data. Inembodiments, a data collection and processing system is provided havingprecise voltage reference for A/D zero reference and havingself-organization of data pools based on utilization and/or yieldmetrics. In embodiments, a data collection and processing system isprovided having precise voltage reference for A/D zero reference andhaving training AI models based on industry-specific feedback. Inembodiments, a data collection and processing system is provided havingprecise voltage reference for A/D zero reference and having aself-organized swarm of industrial data collectors. In embodiments, adata collection and processing system is provided having precise voltagereference for A/D zero reference and having an IoT distributed ledger.In embodiments, a data collection and processing system is providedhaving precise voltage reference for A/D zero reference and having aself-organizing collector. In embodiments, a data collection andprocessing system is provided having precise voltage reference for A/Dzero reference and having a network-sensitive collector. In embodiments,a data collection and processing system is provided having precisevoltage reference for A/D zero reference and having a remotely organizedcollector. In embodiments, a data collection and processing system isprovided having precise voltage reference for A/D zero reference andhaving a self-organizing storage for a multi-sensor data collector. Inembodiments, a data collection and processing system is provided havingprecise voltage reference for A/D zero reference and having aself-organizing network coding for multi-sensor data network. Inembodiments, a data collection and processing system is provided havingprecise voltage reference for A/D zero reference and having a wearablehaptic user interface for an industrial sensor data collector, withvibration, heat, electrical and/or sound outputs. In embodiments, a datacollection and processing system is provided having precise voltagereference for A/D zero reference and having heat maps displayingcollected data for AR/VR. In embodiments, a data collection andprocessing system is provided having precise voltage reference for A/Dzero reference and having automatically tuned AR/VR visualization ofdata collected by a data collector.

In embodiments, a data collection and processing system is providedhaving a phase-lock loop band-pass tracking filter for obtainingslow-speed RPMs and phase information. In embodiments, a data collectionand processing system is provided having a phase-lock loop band-passtracking filter for obtaining slow-speed RPMs and phase information andhaving digital derivation of phase relative to input and triggerchannels using on-board timers. In embodiments, a data collection andprocessing system is provided having a phase-lock loop band-passtracking filter for obtaining slow-speed RPMs and phase information andhaving a peak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection. In embodiments, a datacollection and processing system is provided having a phase-lock loopband-pass tracking filter for obtaining slow-speed RPMs and phaseinformation and having routing of a trigger channel that is either rawor buffered into other analog channels. In embodiments, a datacollection and processing system is provided having a phase-lock loopband-pass tracking filter for obtaining slow-speed RPMs and phaseinformation and having the use of higher input oversampling fordelta-sigma A/D for lower sampling rate outputs to minimize AA filterrequirements. In embodiments, a data collection and processing system isprovided having a phase-lock loop band-pass tracking filter forobtaining slow-speed RPMs and phase information and having the use of aCPLD as a clock-divider for a delta-sigma analog-to-digital converter toachieve lower sampling rates without the need for digital resampling. Inembodiments, a data collection and processing system is provided havinga phase-lock loop band-pass tracking filter for obtaining slow-speedRPMs and phase information and having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates. In embodiments, a data collection andprocessing system is provided having a phase-lock loop band-passtracking filter for obtaining slow-speed RPMs and phase information andhaving storage of calibration data with maintenance history on-boardcard set. In embodiments, a data collection and processing system isprovided having a phase-lock loop band-pass tracking filter forobtaining slow-speed RPMs and phase information and having a rapid routecreation capability using hierarchical templates. In embodiments, a datacollection and processing system is provided having a phase-lock loopband-pass tracking filter for obtaining slow-speed RPMs and phaseinformation and having intelligent management of data collection bands.In embodiments, a data collection and processing system is providedhaving a phase-lock loop band-pass tracking filter for obtainingslow-speed RPMs and phase information and having a neural net expertsystem using intelligent management of data collection bands. Inembodiments, a data collection and processing system is provided havinga phase-lock loop band-pass tracking filter for obtaining slow-speedRPMs and phase information and having use of a database hierarchy insensor data analysis. In embodiments, a data collection and processingsystem is provided having a phase-lock loop band-pass tracking filterfor obtaining slow-speed RPMs and phase information and having an expertsystem GUI graphical approach to defining intelligent data collectionbands and diagnoses for the expert system. In embodiments, a datacollection and processing system is provided having a phase-lock loopband-pass tracking filter for obtaining slow-speed RPMs and phaseinformation and having a graphical approach for back-calculationdefinition. In embodiments, a data collection and processing system isprovided having a phase-lock loop band-pass tracking filter forobtaining slow-speed RPMs and phase information and having proposedbearing analysis methods. In embodiments, a data collection andprocessing system is provided having a phase-lock loop band-passtracking filter for obtaining slow-speed RPMs and phase information andhaving torsional vibration detection/analysis utilizing transitorysignal analysis. In embodiments, a data collection and processing systemis provided having a phase-lock loop band-pass tracking filter forobtaining slow-speed RPMs and phase information and having improvedintegration using both analog and digital methods. In embodiments, adata collection and processing system is provided having a phase-lockloop band-pass tracking filter for obtaining slow-speed RPMs and phaseinformation and having adaptive scheduling techniques for continuousmonitoring of analog data in a local environment. In embodiments, a datacollection and processing system is provided having a phase-lock loopband-pass tracking filter for obtaining slow-speed RPMs and phaseinformation and having data acquisition parking features. Inembodiments, a data collection and processing system is provided havinga phase-lock loop band-pass tracking filter for obtaining slow-speedRPMs and phase information and having a self-sufficient data acquisitionbox. In embodiments, a data collection and processing system is providedhaving a phase-lock loop band-pass tracking filter for obtainingslow-speed RPMs and phase information and having SD card storage. Inembodiments, a data collection and processing system is provided havinga phase-lock loop band-pass tracking filter for obtaining slow-speedRPMs and phase information and having extended onboard statisticalcapabilities for continuous monitoring. In embodiments, a datacollection and processing system is provided having a phase-lock loopband-pass tracking filter for obtaining slow-speed RPMs and phaseinformation and having the use of ambient, local and vibration noise forprediction. In embodiments, a data collection and processing system isprovided having a phase-lock loop band-pass tracking filter forobtaining slow-speed RPMs and phase information and having smart routechanges route based on incoming data or alarms to enable simultaneousdynamic data for analysis or correlation. In embodiments, a datacollection and processing system is provided having a phase-lock loopband-pass tracking filter for obtaining slow-speed RPMs and phaseinformation and having smart ODS and transfer functions. In embodiments,a data collection and processing system is provided having a phase-lockloop band-pass tracking filter for obtaining slow-speed RPMs and phaseinformation and having a hierarchical multiplexer. In embodiments, adata collection and processing system is provided having a phase-lockloop band-pass tracking filter for obtaining slow-speed RPMs and phaseinformation and having identification of sensor overload. Inembodiments, a data collection and processing system is provided havinga phase-lock loop band-pass tracking filter for obtaining slow-speedRPMs and phase information and having RF identification and aninclinometer. In embodiments, a data collection and processing system isprovided having a phase-lock loop band-pass tracking filter forobtaining slow-speed RPMs and phase information and having continuousultrasonic monitoring. In embodiments, a data collection and processingsystem is provided having a phase-lock loop band-pass tracking filterfor obtaining slow-speed RPMs and phase information and havingcloud-based, machine pattern recognition based on fusion of remote,analog industrial sensors. In embodiments, a data collection andprocessing system is provided having a phase-lock loop band-passtracking filter for obtaining slow-speed RPMs and phase information andhaving cloud-based, machine pattern analysis of state information frommultiple analog industrial sensors to provide anticipated stateinformation for an industrial system. In embodiments, a data collectionand processing system is provided having a phase-lock loop band-passtracking filter for obtaining slow-speed RPMs and phase information andhaving cloud-based policy automation engine for IoT, with creation,deployment, and management of IoT devices. In embodiments, a datacollection and processing system is provided having a phase-lock loopband-pass tracking filter for obtaining slow-speed RPMs and phaseinformation and having on-device sensor fusion and data storage forindustrial IoT devices. In embodiments, a data collection and processingsystem is provided having a phase-lock loop band-pass tracking filterfor obtaining slow-speed RPMs and phase information and having aself-organizing data marketplace for industrial IoT data. Inembodiments, a data collection and processing system is provided havinga phase-lock loop band-pass tracking filter for obtaining slow-speedRPMs and phase information and having self-organization of data poolsbased on utilization and/or yield metrics. In embodiments, a datacollection and processing system is provided having a phase-lock loopband-pass tracking filter for obtaining slow-speed RPMs and phaseinformation and having training AI models based on industry-specificfeedback. In embodiments, a data collection and processing system isprovided having a phase-lock loop band-pass tracking filter forobtaining slow-speed RPMs and phase information and having aself-organized swarm of industrial data collectors. In embodiments, adata collection and processing system is provided having a phase-lockloop band-pass tracking filter for obtaining slow-speed RPMs and phaseinformation and having an IoT distributed ledger. In embodiments, a datacollection and processing system is provided having a phase-lock loopband-pass tracking filter for obtaining slow-speed RPMs and phaseinformation and having a self-organizing collector. In embodiments, adata collection and processing system is provided having a phase-lockloop band-pass tracking filter for obtaining slow-speed RPMs and phaseinformation and having a network-sensitive collector. In embodiments, adata collection and processing system is provided having a phase-lockloop band-pass tracking filter for obtaining slow-speed RPMs and phaseinformation and having a remotely organized collector. In embodiments, adata collection and processing system is provided having a phase-lockloop band-pass tracking filter for obtaining slow-speed RPMs and phaseinformation and having a self-organizing storage for a multi-sensor datacollector. In embodiments, a data collection and processing system isprovided having a phase-lock loop band-pass tracking filter forobtaining slow-speed RPMs and phase information and having aself-organizing network coding for multi-sensor data network. Inembodiments, a data collection and processing system is provided havinga phase-lock loop band-pass tracking filter for obtaining slow-speedRPMs and phase information and having a wearable haptic user interfacefor an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs. In embodiments, a data collection andprocessing system is provided having a phase-lock loop band-passtracking filter for obtaining slow-speed RPMs and phase information andhaving heat maps displaying collected data for AR/VR. In embodiments, adata collection and processing system is provided having a phase-lockloop band-pass tracking filter for obtaining slow-speed RPMs and phaseinformation and having automatically tuned AR/VR visualization of datacollected by a data collector.

In embodiments, a data collection and processing system is providedhaving digital derivation of phase relative to input and triggerchannels using on-board timers. In embodiments, a data collection andprocessing system is provided having digital derivation of phaserelative to input and trigger channels using on-board timers and havinga peak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection. In embodiments, a datacollection and processing system is provided having digital derivationof phase relative to input and trigger channels using on-board timersand having routing of a trigger channel that is either raw or bufferedinto other analog channels. In embodiments, a data collection andprocessing system is provided having digital derivation of phaserelative to input and trigger channels using on-board timers and havingthe use of higher input oversampling for delta-sigma A/D for lowersampling rate outputs to minimize AA filter requirements. Inembodiments, a data collection and processing system is provided havingdigital derivation of phase relative to input and trigger channels usingon-board timers and having the use of a CPLD as a clock-divider for adelta-sigma analog-to-digital converter to achieve lower sampling rateswithout the need for digital resampling. In embodiments, a datacollection and processing system is provided having digital derivationof phase relative to input and trigger channels using on-board timersand having long blocks of data at a high-sampling rate as opposed tomultiple sets of data taken at different sampling rates. In embodiments,a data collection and processing system is provided having digitalderivation of phase relative to input and trigger channels usingon-board timers and having storage of calibration data with maintenancehistory on-board card set. In embodiments, a data collection andprocessing system is provided having digital derivation of phaserelative to input and trigger channels using on-board timers and havinga rapid route creation capability using hierarchical templates. Inembodiments, a data collection and processing system is provided havingdigital derivation of phase relative to input and trigger channels usingon-board timers and having intelligent management of data collectionbands. In embodiments, a data collection and processing system isprovided having digital derivation of phase relative to input andtrigger channels using on-board timers and having a neural net expertsystem using intelligent management of data collection bands. Inembodiments, a data collection and processing system is provided havingdigital derivation of phase relative to input and trigger channels usingon-board timers and having use of a database hierarchy in sensor dataanalysis. In embodiments, a data collection and processing system isprovided having digital derivation of phase relative to input andtrigger channels using on-board timers and having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system. In embodiments, a data collection andprocessing system is provided having digital derivation of phaserelative to input and trigger channels using on-board timers and havinga graphical approach for back-calculation definition. In embodiments, adata collection and processing system is provided having digitalderivation of phase relative to input and trigger channels usingon-board timers and having proposed bearing analysis methods. Inembodiments, a data collection and processing system is provided havingdigital derivation of phase relative to input and trigger channels usingon-board timers and having torsional vibration detection/analysisutilizing transitory signal analysis. In embodiments, a data collectionand processing system is provided having digital derivation of phaserelative to input and trigger channels using on-board timers and havingimproved integration using both analog and digital methods. Inembodiments, a data collection and processing system is provided havingdigital derivation of phase relative to input and trigger channels usingon-board timers and having adaptive scheduling techniques for continuousmonitoring of analog data in a local environment. In embodiments, a datacollection and processing system is provided having digital derivationof phase relative to input and trigger channels using on-board timersand having data acquisition parking features. In embodiments, a datacollection and processing system is provided having digital derivationof phase relative to input and trigger channels using on-board timersand having a self-sufficient data acquisition box. In embodiments, adata collection and processing system is provided having digitalderivation of phase relative to input and trigger channels usingon-board timers and having SD card storage. In embodiments, a datacollection and processing system is provided having digital derivationof phase relative to input and trigger channels using on-board timersand having extended onboard statistical capabilities for continuousmonitoring. In embodiments, a data collection and processing system isprovided having digital derivation of phase relative to input andtrigger channels using on-board timers and having the use of ambient,local and vibration noise for prediction. In embodiments, a datacollection and processing system is provided having digital derivationof phase relative to input and trigger channels using on-board timersand having smart route changes route based on incoming data or alarms toenable simultaneous dynamic data for analysis or correlation. Inembodiments, a data collection and processing system is provided havingdigital derivation of phase relative to input and trigger channels usingon-board timers and having smart ODS and transfer functions. Inembodiments, a data collection and processing system is provided havingdigital derivation of phase relative to input and trigger channels usingon-board timers and having a hierarchical multiplexer. In embodiments, adata collection and processing system is provided having digitalderivation of phase relative to input and trigger channels usingon-board timers and having identification of sensor overload. Inembodiments, a data collection and processing system is provided havingdigital derivation of phase relative to input and trigger channels usingon-board timers and having RF identification and an inclinometer. Inembodiments, a data collection and processing system is provided havingdigital derivation of phase relative to input and trigger channels usingon-board timers and having continuous ultrasonic monitoring. Inembodiments, a data collection and processing system is provided havingdigital derivation of phase relative to input and trigger channels usingon-board timers and having cloud-based, machine pattern recognitionbased on fusion of remote, analog industrial sensors. In embodiments, adata collection and processing system is provided having digitalderivation of phase relative to input and trigger channels usingon-board timers and having cloud-based, machine pattern analysis ofstate information from multiple analog industrial sensors to provideanticipated state information for an industrial system. In embodiments,a data collection and processing system is provided having digitalderivation of phase relative to input and trigger channels usingon-board timers and having cloud-based policy automation engine for IoT,with creation, deployment, and management of IoT devices. Inembodiments, a data collection and processing system is provided havingdigital derivation of phase relative to input and trigger channels usingon-board timers and having on-device sensor fusion and data storage forindustrial IoT devices. In embodiments, a data collection and processingsystem is provided having digital derivation of phase relative to inputand trigger channels using on-board timers and having a self-organizingdata marketplace for industrial IoT data. In embodiments, a datacollection and processing system is provided having digital derivationof phase relative to input and trigger channels using on-board timersand having self-organization of data pools based on utilization and/oryield metrics. In embodiments, a data collection and processing systemis provided having digital derivation of phase relative to input andtrigger channels using on-board timers and having training AI modelsbased on industry-specific feedback. In embodiments, a data collectionand processing system is provided having digital derivation of phaserelative to input and trigger channels using on-board timers and havinga self-organized swarm of industrial data collectors. In embodiments, adata collection and processing system is provided having digitalderivation of phase relative to input and trigger channels usingon-board timers and having an IoT distributed ledger. In embodiments, adata collection and processing system is provided having digitalderivation of phase relative to input and trigger channels usingon-board timers and having a self-organizing collector. In embodiments,a data collection and processing system is provided having digitalderivation of phase relative to input and trigger channels usingon-board timers and having a network-sensitive collector. Inembodiments, a data collection and processing system is provided havingdigital derivation of phase relative to input and trigger channels usingon-board timers and having a remotely organized collector. Inembodiments, a data collection and processing system is provided havingdigital derivation of phase relative to input and trigger channels usingon-board timers and having a self-organizing storage for a multi-sensordata collector. In embodiments, a data collection and processing systemis provided having digital derivation of phase relative to input andtrigger channels using on-board timers and having a self-organizingnetwork coding for multi-sensor data network. In embodiments, a datacollection and processing system is provided having digital derivationof phase relative to input and trigger channels using on-board timersand having a wearable haptic user interface for an industrial sensordata collector, with vibration, heat, electrical and/or sound outputs.In embodiments, a data collection and processing system is providedhaving digital derivation of phase relative to input and triggerchannels using on-board timers and having heat maps displaying collecteddata for AR/VR. In embodiments, a data collection and processing systemis provided having digital derivation of phase relative to input andtrigger channels using on-board timers and having automatically tunedAR/VR visualization of data collected by a data collector.

In embodiments, a data collection and processing system is providedhaving a peak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection. In embodiments, a datacollection and processing system is provided having a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection and having routing of a trigger channel that iseither raw or buffered into other analog channels. In embodiments, adata collection and processing system is provided having a peak-detectorfor auto-scaling that is routed into a separate analog-to-digitalconverter for peak detection and having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements. In embodiments, a data collection andprocessing system is provided having a peak-detector for auto-scalingthat is routed into a separate analog-to-digital converter for peakdetection and having the use of a CPLD as a clock-divider for adelta-sigma analog-to-digital converter to achieve lower sampling rateswithout the need for digital resampling. In embodiments, a datacollection and processing system is provided having a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection and having long blocks of data at a high-samplingrate as opposed to multiple sets of data taken at different samplingrates. In embodiments, a data collection and processing system isprovided having a peak-detector for auto-scaling that is routed into aseparate analog-to-digital converter for peak detection and havingstorage of calibration data with maintenance history on-board card set.In embodiments, a data collection and processing system is providedhaving a peak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection and having a rapid routecreation capability using hierarchical templates. In embodiments, a datacollection and processing system is provided having a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection and having intelligent management of data collectionbands. In embodiments, a data collection and processing system isprovided having a peak-detector for auto-scaling that is routed into aseparate analog-to-digital converter for peak detection and having aneural net expert system using intelligent management of data collectionbands. In embodiments, a data collection and processing system isprovided having a peak-detector for auto-scaling that is routed into aseparate analog-to-digital converter for peak detection and having useof a database hierarchy in sensor data analysis. In embodiments, a datacollection and processing system is provided having a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection and having an expert system GUI graphical approach todefining intelligent data collection bands and diagnoses for the expertsystem. In embodiments, a data collection and processing system isprovided having a peak-detector for auto-scaling that is routed into aseparate analog-to-digital converter for peak detection and having agraphical approach for back-calculation definition. In embodiments, adata collection and processing system is provided having a peak-detectorfor auto-scaling that is routed into a separate analog-to-digitalconverter for peak detection and having proposed bearing analysismethods. In embodiments, a data collection and processing system isprovided having a peak-detector for auto-scaling that is routed into aseparate analog-to-digital converter for peak detection and havingtorsional vibration detection/analysis utilizing transitory signalanalysis. In embodiments, a data collection and processing system isprovided having a peak-detector for auto-scaling that is routed into aseparate analog-to-digital converter for peak detection and havingimproved integration using both analog and digital methods. Inembodiments, a data collection and processing system is provided havinga peak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection and having adaptivescheduling techniques for continuous monitoring of analog data in alocal environment. In embodiments, a data collection and processingsystem is provided having a peak-detector for auto-scaling that isrouted into a separate analog-to-digital converter for peak detectionand having data acquisition parking features. In embodiments, a datacollection and processing system is provided having a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection and having a self-sufficient data acquisition box. Inembodiments, a data collection and processing system is provided havinga peak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection and having SD cardstorage. In embodiments, a data collection and processing system isprovided having a peak-detector for auto-scaling that is routed into aseparate analog-to-digital converter for peak detection and havingextended onboard statistical capabilities for continuous monitoring. Inembodiments, a data collection and processing system is provided havinga peak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection and having the use ofambient, local and vibration noise for prediction. In embodiments, adata collection and processing system is provided having a peak-detectorfor auto-scaling that is routed into a separate analog-to-digitalconverter for peak detection and having smart route changes route basedon incoming data or alarms to enable simultaneous dynamic data foranalysis or correlation. In embodiments, a data collection andprocessing system is provided having a peak-detector for auto-scalingthat is routed into a separate analog-to-digital converter for peakdetection and having smart ODS and transfer functions. In embodiments, adata collection and processing system is provided having a peak-detectorfor auto-scaling that is routed into a separate analog-to-digitalconverter for peak detection and having a hierarchical multiplexer. Inembodiments, a data collection and processing system is provided havinga peak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection and having identificationof sensor overload. In embodiments, a data collection and processingsystem is provided having a peak-detector for auto-scaling that isrouted into a separate analog-to-digital converter for peak detectionand having RF identification and an inclinometer. In embodiments, a datacollection and processing system is provided having a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection and having continuous ultrasonic monitoring. Inembodiments, a data collection and processing system is provided havinga peak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection and having cloud-based,machine pattern recognition based on fusion of remote, analog industrialsensors. In embodiments, a data collection and processing system isprovided having a peak-detector for auto-scaling that is routed into aseparate analog-to-digital converter for peak detection and havingcloud-based, machine pattern analysis of state information from multipleanalog industrial sensors to provide anticipated state information foran industrial system. In embodiments, a data collection and processingsystem is provided having a peak-detector for auto-scaling that isrouted into a separate analog-to-digital converter for peak detectionand having cloud-based policy automation engine for IoT, with creation,deployment, and management of IoT devices. In embodiments, a datacollection and processing system is provided having a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection and having on-device sensor fusion and data storagefor industrial IoT devices. In embodiments, a data collection andprocessing system is provided having a peak-detector for auto-scalingthat is routed into a separate analog-to-digital converter for peakdetection and having a self-organizing data marketplace for industrialIoT data. In embodiments, a data collection and processing system isprovided having a peak-detector for auto-scaling that is routed into aseparate analog-to-digital converter for peak detection and havingself-organization of data pools based on utilization and/or yieldmetrics. In embodiments, a data collection and processing system isprovided having a peak-detector for auto-scaling that is routed into aseparate analog-to-digital converter for peak detection and havingtraining AI models based on industry-specific feedback. In embodiments,a data collection and processing system is provided having apeak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection and having aself-organized swarm of industrial data collectors. In embodiments, adata collection and processing system is provided having a peak-detectorfor auto-scaling that is routed into a separate analog-to-digitalconverter for peak detection and having an IoT distributed ledger. Inembodiments, a data collection and processing system is provided havinga peak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection and having aself-organizing collector. In embodiments, a data collection andprocessing system is provided having a peak-detector for auto-scalingthat is routed into a separate analog-to-digital converter for peakdetection and having a network-sensitive collector. In embodiments, adata collection and processing system is provided having a peak-detectorfor auto-scaling that is routed into a separate analog-to-digitalconverter for peak detection and having a remotely organized collector.In embodiments, a data collection and processing system is providedhaving a peak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection and having aself-organizing storage for a multi-sensor data collector. Inembodiments, a data collection and processing system is provided havinga peak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection and having aself-organizing network coding for multi-sensor data network. Inembodiments, a data collection and processing system is provided havinga peak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection and having a wearablehaptic user interface for an industrial sensor data collector, withvibration, heat, electrical and/or sound outputs. In embodiments, a datacollection and processing system is provided having a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection and having heat maps displaying collected data forAR/VR. In embodiments, a data collection and processing system isprovided having a peak-detector for auto-scaling that is routed into aseparate analog-to-digital converter for peak detection and havingautomatically tuned AR/VR visualization of data collected by a datacollector.

In embodiments, a data collection and processing system is providedhaving routing of a trigger channel that is either raw or buffered intoother analog channels. In embodiments, a data collection and processingsystem is provided having routing of a trigger channel that is eitherraw or buffered into other analog channels and having the use of higherinput oversampling for delta-sigma A/D for lower sampling rate outputsto minimize AA filter requirements. In embodiments, a data collectionand processing system is provided having routing of a trigger channelthat is either raw or buffered into other analog channels and having theuse of a CPLD as a clock-divider for a delta-sigma analog-to-digitalconverter to achieve lower sampling rates without the need for digitalresampling. In embodiments, a data collection and processing system isprovided having routing of a trigger channel that is either raw orbuffered into other analog channels and having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates. In embodiments, a data collection andprocessing system is provided having routing of a trigger channel thatis either raw or buffered into other analog channels and having storageof calibration data with maintenance history on-board card set. Inembodiments, a data collection and processing system is provided havingrouting of a trigger channel that is either raw or buffered into otheranalog channels and having a rapid route creation capability usinghierarchical templates. In embodiments, a data collection and processingsystem is provided having routing of a trigger channel that is eitherraw or buffered into other analog channels and having intelligentmanagement of data collection bands. In embodiments, a data collectionand processing system is provided having routing of a trigger channelthat is either raw or buffered into other analog channels and having aneural net expert system using intelligent management of data collectionbands. In embodiments, a data collection and processing system isprovided having routing of a trigger channel that is either raw orbuffered into other analog channels and having use of a databasehierarchy in sensor data analysis. In embodiments, a data collection andprocessing system is provided having routing of a trigger channel thatis either raw or buffered into other analog channels and having anexpert system GUI graphical approach to defining intelligent datacollection bands and diagnoses for the expert system. In embodiments, adata collection and processing system is provided having routing of atrigger channel that is either raw or buffered into other analogchannels and having a graphical approach for back-calculationdefinition. In embodiments, a data collection and processing system isprovided having routing of a trigger channel that is either raw orbuffered into other analog channels and having proposed bearing analysismethods. In embodiments, a data collection and processing system isprovided having routing of a trigger channel that is either raw orbuffered into other analog channels and having torsional vibrationdetection/analysis utilizing transitory signal analysis. In embodiments,a data collection and processing system is provided having routing of atrigger channel that is either raw or buffered into other analogchannels and having improved integration using both analog and digitalmethods. In embodiments, a data collection and processing system isprovided having routing of a trigger channel that is either raw orbuffered into other analog channels and having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment. In embodiments, a data collection and processing system isprovided having routing of a trigger channel that is either raw orbuffered into other analog channels and having data acquisition parkingfeatures. In embodiments, a data collection and processing system isprovided having routing of a trigger channel that is either raw orbuffered into other analog channels and having a self-sufficient dataacquisition box. In embodiments, a data collection and processing systemis provided having routing of a trigger channel that is either raw orbuffered into other analog channels and having SD card storage. Inembodiments, a data collection and processing system is provided havingrouting of a trigger channel that is either raw or buffered into otheranalog channels and having extended onboard statistical capabilities forcontinuous monitoring. In embodiments, a data collection and processingsystem is provided having routing of a trigger channel that is eitherraw or buffered into other analog channels and having the use ofambient, local and vibration noise for prediction. In embodiments, adata collection and processing system is provided having routing of atrigger channel that is either raw or buffered into other analogchannels and having smart route changes route based on incoming data oralarms to enable simultaneous dynamic data for analysis or correlation.In embodiments, a data collection and processing system is providedhaving routing of a trigger channel that is either raw or buffered intoother analog channels and having smart ODS and transfer functions. Inembodiments, a data collection and processing system is provided havingrouting of a trigger channel that is either raw or buffered into otheranalog channels and having a hierarchical multiplexer. In embodiments, adata collection and processing system is provided having routing of atrigger channel that is either raw or buffered into other analogchannels and having identification of sensor overload. In embodiments, adata collection and processing system is provided having routing of atrigger channel that is either raw or buffered into other analogchannels and having RF identification and an inclinometer. Inembodiments, a data collection and processing system is provided havingrouting of a trigger channel that is either raw or buffered into otheranalog channels and having continuous ultrasonic monitoring. Inembodiments, a data collection and processing system is provided havingrouting of a trigger channel that is either raw or buffered into otheranalog channels and having cloud-based, machine pattern recognitionbased on fusion of remote, analog industrial sensors. In embodiments, adata collection and processing system is provided having routing of atrigger channel that is either raw or buffered into other analogchannels and having cloud-based, machine pattern analysis of stateinformation from multiple analog industrial sensors to provideanticipated state information for an industrial system. In embodiments,a data collection and processing system is provided having routing of atrigger channel that is either raw or buffered into other analogchannels and having cloud-based policy automation engine for IoT, withcreation, deployment, and management of IoT devices. In embodiments, adata collection and processing system is provided having routing of atrigger channel that is either raw or buffered into other analogchannels and having on-device sensor fusion and data storage forindustrial IoT devices. In embodiments, a data collection and processingsystem is provided having routing of a trigger channel that is eitherraw or buffered into other analog channels and having a self-organizingdata marketplace for industrial IoT data. In embodiments, a datacollection and processing system is provided having routing of a triggerchannel that is either raw or buffered into other analog channels andhaving self-organization of data pools based on utilization and/or yieldmetrics. In embodiments, a data collection and processing system isprovided having routing of a trigger channel that is either raw orbuffered into other analog channels and having training AI models basedon industry-specific feedback. In embodiments, a data collection andprocessing system is provided having routing of a trigger channel thatis either raw or buffered into other analog channels and having aself-organized swarm of industrial data collectors. In embodiments, adata collection and processing system is provided having routing of atrigger channel that is either raw or buffered into other analogchannels and having an IoT distributed ledger. In embodiments, a datacollection and processing system is provided having routing of a triggerchannel that is either raw or buffered into other analog channels andhaving a self-organizing collector. In embodiments, a data collectionand processing system is provided having routing of a trigger channelthat is either raw or buffered into other analog channels and having anetwork-sensitive collector. In embodiments, a data collection andprocessing system is provided having routing of a trigger channel thatis either raw or buffered into other analog channels and having aremotely organized collector. In embodiments, a data collection andprocessing system is provided having routing of a trigger channel thatis either raw or buffered into other analog channels and having aself-organizing storage for a multi-sensor data collector. Inembodiments, a data collection and processing system is provided havingrouting of a trigger channel that is either raw or buffered into otheranalog channels and having a self-organizing network coding formulti-sensor data network. In embodiments, a data collection andprocessing system is provided having routing of a trigger channel thatis either raw or buffered into other analog channels and having awearable haptic user interface for an industrial sensor data collector,with vibration, heat, electrical and/or sound outputs. In embodiments, adata collection and processing system is provided having routing of atrigger channel that is either raw or buffered into other analogchannels and having heat maps displaying collected data for AR/VR. Inembodiments, a data collection and processing system is provided havingrouting of a trigger channel that is either raw or buffered into otheranalog channels and having automatically tuned AR/VR visualization ofdata collected by a data collector.

In embodiments, a data collection and processing system is providedhaving the use of higher input oversampling for delta-sigma A/D forlower sampling rate outputs to minimize AA filter requirements. Inembodiments, a data collection and processing system is provided havingthe use of higher input oversampling for delta-sigma A/D for lowersampling rate outputs to minimize AA filter requirements and having theuse of a CPLD as a clock-divider for a delta-sigma analog-to-digitalconverter to achieve lower sampling rates without the need for digitalresampling. In embodiments, a data collection and processing system isprovided having the use of higher input oversampling for delta-sigma A/Dfor lower sampling rate outputs to minimize AA filter requirements andhaving long blocks of data at a high-sampling rate as opposed tomultiple sets of data taken at different sampling rates. In embodiments,a data collection and processing system is provided having the use ofhigher input oversampling for delta-sigma A/D for lower sampling rateoutputs to minimize AA filter requirements and having storage ofcalibration data with maintenance history on-board card set. Inembodiments, a data collection and processing system is provided havingthe use of higher input oversampling for delta-sigma A/D for lowersampling rate outputs to minimize AA filter requirements and having arapid route creation capability using hierarchical templates. Inembodiments, a data collection and processing system is provided havingthe use of higher input oversampling for delta-sigma A/D for lowersampling rate outputs to minimize AA filter requirements and havingintelligent management of data collection bands. In embodiments, a datacollection and processing system is provided having the use of higherinput oversampling for delta-sigma A/D for lower sampling rate outputsto minimize AA filter requirements and having a neural net expert systemusing intelligent management of data collection bands. In embodiments, adata collection and processing system is provided having the use ofhigher input oversampling for delta-sigma A/D for lower sampling rateoutputs to minimize AA filter requirements and having use of a databasehierarchy in sensor data analysis. In embodiments, a data collection andprocessing system is provided having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements and having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system. In embodiments, a data collection andprocessing system is provided having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements and having a graphical approach forback-calculation definition. In embodiments, a data collection andprocessing system is provided having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements and having proposed bearing analysismethods. In embodiments, a data collection and processing system isprovided having the use of higher input oversampling for delta-sigma A/Dfor lower sampling rate outputs to minimize AA filter requirements andhaving torsional vibration detection/analysis utilizing transitorysignal analysis. In embodiments, a data collection and processing systemis provided having the use of higher input oversampling for delta-sigmaA/D for lower sampling rate outputs to minimize AA filter requirementsand having improved integration using both analog and digital methods.In embodiments, a data collection and processing system is providedhaving the use of higher input oversampling for delta-sigma A/D forlower sampling rate outputs to minimize AA filter requirements andhaving adaptive scheduling techniques for continuous monitoring ofanalog data in a local environment. In embodiments, a data collectionand processing system is provided having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements and having data acquisition parkingfeatures. In embodiments, a data collection and processing system isprovided having the use of higher input oversampling for delta-sigma A/Dfor lower sampling rate outputs to minimize AA filter requirements andhaving a self-sufficient data acquisition box. In embodiments, a datacollection and processing system is provided having the use of higherinput oversampling for delta-sigma A/D for lower sampling rate outputsto minimize AA filter requirements and having SD card storage. Inembodiments, a data collection and processing system is provided havingthe use of higher input oversampling for delta-sigma A/D for lowersampling rate outputs to minimize AA filter requirements and havingextended onboard statistical capabilities for continuous monitoring. Inembodiments, a data collection and processing system is provided havingthe use of higher input oversampling for delta-sigma A/D for lowersampling rate outputs to minimize AA filter requirements and having theuse of ambient, local and vibration noise for prediction. Inembodiments, a data collection and processing system is provided havingthe use of higher input oversampling for delta-sigma A/D for lowersampling rate outputs to minimize AA filter requirements and havingsmart route changes route based on incoming data or alarms to enablesimultaneous dynamic data for analysis or correlation. In embodiments, adata collection and processing system is provided having the use ofhigher input oversampling for delta-sigma A/D for lower sampling rateoutputs to minimize AA filter requirements and having smart ODS andtransfer functions. In embodiments, a data collection and processingsystem is provided having the use of higher input oversampling fordelta-sigma A/D for lower sampling rate outputs to minimize AA filterrequirements and having a hierarchical multiplexer. In embodiments, adata collection and processing system is provided having the use ofhigher input oversampling for delta-sigma A/D for lower sampling rateoutputs to minimize AA filter requirements and having identification ofsensor overload. In embodiments, a data collection and processing systemis provided having the use of higher input oversampling for delta-sigmaA/D for lower sampling rate outputs to minimize AA filter requirementsand having RF identification and an inclinometer. In embodiments, a datacollection and processing system is provided having the use of higherinput oversampling for delta-sigma A/D for lower sampling rate outputsto minimize AA filter requirements and having continuous ultrasonicmonitoring. In embodiments, a data collection and processing system isprovided having the use of higher input oversampling for delta-sigma A/Dfor lower sampling rate outputs to minimize AA filter requirements andhaving cloud-based, machine pattern recognition based on fusion ofremote, analog industrial sensors. In embodiments, a data collection andprocessing system is provided having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements and having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system. Inembodiments, a data collection and processing system is provided havingthe use of higher input oversampling for delta-sigma A/D for lowersampling rate outputs to minimize AA filter requirements and havingcloud-based policy automation engine for IoT, with creation, deployment,and management of IoT devices. In embodiments, a data collection andprocessing system is provided having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements and having on-device sensor fusion anddata storage for industrial IoT devices. In embodiments, a datacollection and processing system is provided having the use of higherinput oversampling for delta-sigma A/D for lower sampling rate outputsto minimize AA filter requirements and having a self-organizing datamarketplace for industrial IoT data. In embodiments, a data collectionand processing system is provided having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements and having self-organization of datapools based on utilization and/or yield metrics. In embodiments, a datacollection and processing system is provided having the use of higherinput oversampling for delta-sigma A/D for lower sampling rate outputsto minimize AA filter requirements and having training AI models basedon industry-specific feedback. In embodiments, a data collection andprocessing system is provided having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements and having a self-organized swarm ofindustrial data collectors. In embodiments, a data collection andprocessing system is provided having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements and having an IoT distributed ledger. Inembodiments, a data collection and processing system is provided havingthe use of higher input oversampling for delta-sigma A/D for lowersampling rate outputs to minimize AA filter requirements and having aself-organizing collector. In embodiments, a data collection andprocessing system is provided having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements and having a network-sensitivecollector. In embodiments, a data collection and processing system isprovided having the use of higher input oversampling for delta-sigma A/Dfor lower sampling rate outputs to minimize AA filter requirements andhaving a remotely organized collector. In embodiments, a data collectionand processing system is provided having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements and having a self-organizing storage fora multi-sensor data collector. In embodiments, a data collection andprocessing system is provided having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements and having a self-organizing networkcoding for multi-sensor data network. In embodiments, a data collectionand processing system is provided having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements and having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs. In embodiments, a data collection andprocessing system is provided having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements and having heat maps displayingcollected data for AR/VR. In embodiments, a data collection andprocessing system is provided having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements and having automatically tuned AR/VRvisualization of data collected by a data collector.

In embodiments, a data collection and processing system is providedhaving the use of a CPLD as a clock-divider for a delta-sigmaanalog-to-digital converter to achieve lower sampling rates without theneed for digital resampling. In embodiments, a data collection andprocessing system is provided having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling and havinglong blocks of data at a high-sampling rate as opposed to multiple setsof data taken at different sampling rates. In embodiments, a datacollection and processing system is provided having the use of a CPLD asa clock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling and havingstorage of calibration data with maintenance history on-board card set.In embodiments, a data collection and processing system is providedhaving the use of a CPLD as a clock-divider for a delta-sigmaanalog-to-digital converter to achieve lower sampling rates without theneed for digital resampling and having a rapid route creation capabilityusing hierarchical templates. In embodiments, a data collection andprocessing system is provided having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling and havingintelligent management of data collection bands. In embodiments, a datacollection and processing system is provided having the use of a CPLD asa clock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling and havinga neural net expert system using intelligent management of datacollection bands. In embodiments, a data collection and processingsystem is provided having the use of a CPLD as a clock-divider for adelta-sigma analog-to-digital converter to achieve lower sampling rateswithout the need for digital resampling and having use of a databasehierarchy in sensor data analysis. In embodiments, a data collection andprocessing system is provided having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling and havingan expert system GUI graphical approach to defining intelligent datacollection bands and diagnoses for the expert system. In embodiments, adata collection and processing system is provided having the use of aCPLD as a clock-divider for a delta-sigma analog-to-digital converter toachieve lower sampling rates without the need for digital resampling andhaving a graphical approach for back-calculation definition. Inembodiments, a data collection and processing system is provided havingthe use of a CPLD as a clock-divider for a delta-sigma analog-to-digitalconverter to achieve lower sampling rates without the need for digitalresampling and having proposed bearing analysis methods. In embodiments,a data collection and processing system is provided having the use of aCPLD as a clock-divider for a delta-sigma analog-to-digital converter toachieve lower sampling rates without the need for digital resampling andhaving torsional vibration detection/analysis utilizing transitorysignal analysis. In embodiments, a data collection and processing systemis provided having the use of a CPLD as a clock-divider for adelta-sigma analog-to-digital converter to achieve lower sampling rateswithout the need for digital resampling and having improved integrationusing both analog and digital methods. In embodiments, a data collectionand processing system is provided having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling and havingadaptive scheduling techniques for continuous monitoring of analog datain a local environment. In embodiments, a data collection and processingsystem is provided having the use of a CPLD as a clock-divider for adelta-sigma analog-to-digital converter to achieve lower sampling rateswithout the need for digital resampling and having data acquisitionparking features. In embodiments, a data collection and processingsystem is provided having the use of a CPLD as a clock-divider for adelta-sigma analog-to-digital converter to achieve lower sampling rateswithout the need for digital resampling and having a self-sufficientdata acquisition box. In embodiments, a data collection and processingsystem is provided having the use of a CPLD as a clock-divider for adelta-sigma analog-to-digital converter to achieve lower sampling rateswithout the need for digital resampling and having SD card storage. Inembodiments, a data collection and processing system is provided havingthe use of a CPLD as a clock-divider for a delta-sigma analog-to-digitalconverter to achieve lower sampling rates without the need for digitalresampling and having extended onboard statistical capabilities forcontinuous monitoring. In embodiments, a data collection and processingsystem is provided having the use of a CPLD as a clock-divider for adelta-sigma analog-to-digital converter to achieve lower sampling rateswithout the need for digital resampling and having the use of ambient,local and vibration noise for prediction. In embodiments, a datacollection and processing system is provided having the use of a CPLD asa clock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling and havingsmart route changes route based on incoming data or alarms to enablesimultaneous dynamic data for analysis or correlation. In embodiments, adata collection and processing system is provided having the use of aCPLD as a clock-divider for a delta-sigma analog-to-digital converter toachieve lower sampling rates without the need for digital resampling andhaving smart ODS and transfer functions. In embodiments, a datacollection and processing system is provided having the use of a CPLD asa clock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling and havinga hierarchical multiplexer. In embodiments, a data collection andprocessing system is provided having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling and havingidentification of sensor overload. In embodiments, a data collection andprocessing system is provided having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling and havingRF identification and an inclinometer. In embodiments, a data collectionand processing system is provided having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling and havingcontinuous ultrasonic monitoring. In embodiments, a data collection andprocessing system is provided having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling and havingcloud-based, machine pattern recognition based on fusion of remote,analog industrial sensors. In embodiments, a data collection andprocessing system is provided having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling and havingcloud-based, machine pattern analysis of state information from multipleanalog industrial sensors to provide anticipated state information foran industrial system. In embodiments, a data collection and processingsystem is provided having the use of a CPLD as a clock-divider for adelta-sigma analog-to-digital converter to achieve lower sampling rateswithout the need for digital resampling and having cloud-based policyautomation engine for IoT, with creation, deployment, and management ofIoT devices. In embodiments, a data collection and processing system isprovided having the use of a CPLD as a clock-divider for a delta-sigmaanalog-to-digital converter to achieve lower sampling rates without theneed for digital resampling and having on-device sensor fusion and datastorage for industrial IoT devices. In embodiments, a data collectionand processing system is provided having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling and havinga self-organizing data marketplace for industrial IoT data. Inembodiments, a data collection and processing system is provided havingthe use of a CPLD as a clock-divider for a delta-sigma analog-to-digitalconverter to achieve lower sampling rates without the need for digitalresampling and having self-organization of data pools based onutilization and/or yield metrics. In embodiments, a data collection andprocessing system is provided having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling and havingtraining AI models based on industry-specific feedback. In embodiments,a data collection and processing system is provided having the use of aCPLD as a clock-divider for a delta-sigma analog-to-digital converter toachieve lower sampling rates without the need for digital resampling andhaving a self-organized swarm of industrial data collectors. Inembodiments, a data collection and processing system is provided havingthe use of a CPLD as a clock-divider for a delta-sigma analog-to-digitalconverter to achieve lower sampling rates without the need for digitalresampling and having an IoT distributed ledger. In embodiments, a datacollection and processing system is provided having the use of a CPLD asa clock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling and havinga self-organizing collector. In embodiments, a data collection andprocessing system is provided having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling and havinga network-sensitive collector. In embodiments, a data collection andprocessing system is provided having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling and havinga remotely organized collector. In embodiments, a data collection andprocessing system is provided having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling and havinga self-organizing storage for a multi-sensor data collector. Inembodiments, a data collection and processing system is provided havingthe use of a CPLD as a clock-divider for a delta-sigma analog-to-digitalconverter to achieve lower sampling rates without the need for digitalresampling and having a self-organizing network coding for multi-sensordata network. In embodiments, a data collection and processing system isprovided having the use of a CPLD as a clock-divider for a delta-sigmaanalog-to-digital converter to achieve lower sampling rates without theneed for digital resampling and having a wearable haptic user interfacefor an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs. In embodiments, a data collection andprocessing system is provided having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling and havingheat maps displaying collected data for AR/VR. In embodiments, a datacollection and processing system is provided having the use of a CPLD asa clock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling and havingautomatically tuned AR/VR visualization of data collected by a datacollector.

In embodiments, a data collection and processing system is providedhaving long blocks of data at a high-sampling rate as opposed tomultiple sets of data taken at different sampling rates. In embodiments,a data collection and processing system is provided having long blocksof data at a high-sampling rate as opposed to multiple sets of datataken at different sampling rates and having storage of calibration datawith maintenance history on-board card set. In embodiments, a datacollection and processing system is provided having long blocks of dataat a high-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates and having a rapid route creation capabilityusing hierarchical templates. In embodiments, a data collection andprocessing system is provided having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates and having intelligent management of datacollection bands. In embodiments, a data collection and processingsystem is provided having long blocks of data at a high-sampling rate asopposed to multiple sets of data taken at different sampling rates andhaving a neural net expert system using intelligent management of datacollection bands. In embodiments, a data collection and processingsystem is provided having long blocks of data at a high-sampling rate asopposed to multiple sets of data taken at different sampling rates andhaving use of a database hierarchy in sensor data analysis. Inembodiments, a data collection and processing system is provided havinglong blocks of data at a high-sampling rate as opposed to multiple setsof data taken at different sampling rates and having an expert systemGUI graphical approach to defining intelligent data collection bands anddiagnoses for the expert system. In embodiments, a data collection andprocessing system is provided having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates and having a graphical approach forback-calculation definition. In embodiments, a data collection andprocessing system is provided having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates and having proposed bearing analysis methods.In embodiments, a data collection and processing system is providedhaving long blocks of data at a high-sampling rate as opposed tomultiple sets of data taken at different sampling rates and havingtorsional vibration detection/analysis utilizing transitory signalanalysis. In embodiments, a data collection and processing system isprovided having long blocks of data at a high-sampling rate as opposedto multiple sets of data taken at different sampling rates and havingimproved integration using both analog and digital methods. Inembodiments, a data collection and processing system is provided havinglong blocks of data at a high-sampling rate as opposed to multiple setsof data taken at different sampling rates and having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment. In embodiments, a data collection and processing system isprovided having long blocks of data at a high-sampling rate as opposedto multiple sets of data taken at different sampling rates and havingdata acquisition parking features. In embodiments, a data collection andprocessing system is provided having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates and having a self-sufficient data acquisitionbox. In embodiments, a data collection and processing system is providedhaving long blocks of data at a high-sampling rate as opposed tomultiple sets of data taken at different sampling rates and having SDcard storage. In embodiments, a data collection and processing system isprovided having long blocks of data at a high-sampling rate as opposedto multiple sets of data taken at different sampling rates and havingextended onboard statistical capabilities for continuous monitoring. Inembodiments, a data collection and processing system is provided havinglong blocks of data at a high-sampling rate as opposed to multiple setsof data taken at different sampling rates and having the use of ambient,local and vibration noise for prediction. In embodiments, a datacollection and processing system is provided having long blocks of dataat a high-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates and having smart route changes route based onincoming data or alarms to enable simultaneous dynamic data for analysisor correlation. In embodiments, a data collection and processing systemis provided having long blocks of data at a high-sampling rate asopposed to multiple sets of data taken at different sampling rates andhaving smart ODS and transfer functions. In embodiments, a datacollection and processing system is provided having long blocks of dataat a high-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates and having a hierarchical multiplexer. Inembodiments, a data collection and processing system is provided havinglong blocks of data at a high-sampling rate as opposed to multiple setsof data taken at different sampling rates and having identification ofsensor overload. In embodiments, a data collection and processing systemis provided having long blocks of data at a high-sampling rate asopposed to multiple sets of data taken at different sampling rates andhaving RF identification and an inclinometer. In embodiments, a datacollection and processing system is provided having long blocks of dataat a high-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates and having continuous ultrasonic monitoring. Inembodiments, a data collection and processing system is provided havinglong blocks of data at a high-sampling rate as opposed to multiple setsof data taken at different sampling rates and having cloud-based,machine pattern recognition based on fusion of remote, analog industrialsensors. In embodiments, a data collection and processing system isprovided having long blocks of data at a high-sampling rate as opposedto multiple sets of data taken at different sampling rates and havingcloud-based, machine pattern analysis of state information from multipleanalog industrial sensors to provide anticipated state information foran industrial system. In embodiments, a data collection and processingsystem is provided having long blocks of data at a high-sampling rate asopposed to multiple sets of data taken at different sampling rates andhaving cloud-based policy automation engine for IoT, with creation,deployment, and management of IoT devices. In embodiments, a datacollection and processing system is provided having long blocks of dataat a high-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates and having on-device sensor fusion and datastorage for industrial IoT devices. In embodiments, a data collectionand processing system is provided having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates and having a self-organizing data marketplacefor industrial IoT data. In embodiments, a data collection andprocessing system is provided having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates and having self-organization of data poolsbased on utilization and/or yield metrics. In embodiments, a datacollection and processing system is provided having long blocks of dataat a high-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates and having training AI models based onindustry-specific feedback. In embodiments, a data collection andprocessing system is provided having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates and having a self-organized swarm of industrialdata collectors. In embodiments, a data collection and processing systemis provided having long blocks of data at a high-sampling rate asopposed to multiple sets of data taken at different sampling rates andhaving an IoT distributed ledger. In embodiments, a data collection andprocessing system is provided having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates and having a self-organizing collector. Inembodiments, a data collection and processing system is provided havinglong blocks of data at a high-sampling rate as opposed to multiple setsof data taken at different sampling rates and having a network-sensitivecollector. In embodiments, a data collection and processing system isprovided having long blocks of data at a high-sampling rate as opposedto multiple sets of data taken at different sampling rates and having aremotely organized collector. In embodiments, a data collection andprocessing system is provided having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates and having a self-organizing storage for amulti-sensor data collector. In embodiments, a data collection andprocessing system is provided having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates and having a self-organizing network coding formulti-sensor data network. In embodiments, a data collection andprocessing system is provided having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates and having a wearable haptic user interface foran industrial sensor data collector, with vibration, heat, electrical,and/or sound outputs. In embodiments, a data collection and processingsystem is provided having long blocks of data at a high-sampling rate asopposed to multiple sets of data taken at different sampling rates andhaving heat maps displaying collected data for AR/VR. In embodiments, adata collection and processing system is provided having long blocks ofdata at a high-sampling rate as opposed to multiple sets of data takenat different sampling rates and having automatically tuned AR/VRvisualization of data collected by a data collector.

In embodiments, a data collection and processing system is providedhaving storage of calibration data with maintenance history on-boardcard set. In embodiments, a data collection and processing system isprovided having storage of calibration data with maintenance historyon-board card set and having a rapid route creation capability usinghierarchical templates. In embodiments, a data collection and processingsystem is provided having storage of calibration data with maintenancehistory on-board card set and having intelligent management of datacollection bands. In embodiments, a data collection and processingsystem is provided having storage of calibration data with maintenancehistory on-board card set and having a neural net expert system usingintelligent management of data collection bands. In embodiments, a datacollection and processing system is provided having storage ofcalibration data with maintenance history on-board card set and havinguse of a database hierarchy in sensor data analysis. In embodiments, adata collection and processing system is provided having storage ofcalibration data with maintenance history on-board card set and havingan expert system GUI graphical approach to defining intelligent datacollection bands and diagnoses for the expert system. In embodiments, adata collection and processing system is provided having storage ofcalibration data with maintenance history on-board card set and having agraphical approach for back-calculation definition. In embodiments, adata collection and processing system is provided having storage ofcalibration data with maintenance history on-board card set and havingproposed bearing analysis methods. In embodiments, a data collection andprocessing system is provided having storage of calibration data withmaintenance history on-board card set and having torsional vibrationdetection/analysis utilizing transitory signal analysis. In embodiments,a data collection and processing system is provided having storage ofcalibration data with maintenance history on-board card set and havingimproved integration using both analog and digital methods. Inembodiments, a data collection and processing system is provided havingstorage of calibration data with maintenance history on-board card setand having adaptive scheduling techniques for continuous monitoring ofanalog data in a local environment. In embodiments, a data collectionand processing system is provided having storage of calibration datawith maintenance history on-board card set and having data acquisitionparking features. In embodiments, a data collection and processingsystem is provided having storage of calibration data with maintenancehistory on-board card set and having a self-sufficient data acquisitionbox. In embodiments, a data collection and processing system is providedhaving storage of calibration data with maintenance history on-boardcard set and having SD card storage. In embodiments, a data collectionand processing system is provided having storage of calibration datawith maintenance history on-board card set and having extended onboardstatistical capabilities for continuous monitoring. In embodiments, adata collection and processing system is provided having storage ofcalibration data with maintenance history on-board card set and havingthe use of ambient, local and vibration noise for prediction. Inembodiments, a data collection and processing system is provided havingstorage of calibration data with maintenance history on-board card setand having smart route changes route based on incoming data or alarms toenable simultaneous dynamic data for analysis or correlation. Inembodiments, a data collection and processing system is provided havingstorage of calibration data with maintenance history on-board card setand having smart ODS and transfer functions. In embodiments, a datacollection and processing system is provided having storage ofcalibration data with maintenance history on-board card set and having ahierarchical multiplexer. In embodiments, a data collection andprocessing system is provided having storage of calibration data withmaintenance history on-board card set and having identification ofsensor overload. In embodiments, a data collection and processing systemis provided having storage of calibration data with maintenance historyon-board card set and having RF identification and an inclinometer. Inembodiments, a data collection and processing system is provided havingstorage of calibration data with maintenance history on-board card setand having continuous ultrasonic monitoring. In embodiments, a datacollection and processing system is provided having storage ofcalibration data with maintenance history on-board card set and havingcloud-based, machine pattern recognition based on fusion of remote,analog industrial sensors. In embodiments, a data collection andprocessing system is provided having storage of calibration data withmaintenance history on-board card set and having cloud-based, machinepattern analysis of state information from multiple analog industrialsensors to provide anticipated state information for an industrialsystem. In embodiments, a data collection and processing system isprovided having storage of calibration data with maintenance historyon-board card set and having cloud-based policy automation engine forIoT, with creation, deployment, and management of IoT devices. Inembodiments, a data collection and processing system is provided havingstorage of calibration data with maintenance history on-board card setand having on-device sensor fusion and data storage for industrial IoTdevices. In embodiments, a data collection and processing system isprovided having storage of calibration data with maintenance historyon-board card set and having a self-organizing data marketplace forindustrial IoT data. In embodiments, a data collection and processingsystem is provided having storage of calibration data with maintenancehistory on-board card set and having self-organization of data poolsbased on utilization and/or yield metrics. In embodiments, a datacollection and processing system is provided having storage ofcalibration data with maintenance history on-board card set and havingtraining AI models based on industry-specific feedback. In embodiments,a data collection and processing system is provided having storage ofcalibration data with maintenance history on-board card set and having aself-organized swarm of industrial data collectors. In embodiments, adata collection and processing system is provided having storage ofcalibration data with maintenance history on-board card set and havingan IoT distributed ledger. In embodiments, a data collection andprocessing system is provided having storage of calibration data withmaintenance history on-board card set and having a self-organizingcollector. In embodiments, a data collection and processing system isprovided having storage of calibration data with maintenance historyon-board card set and having a network-sensitive collector. Inembodiments, a data collection and processing system is provided havingstorage of calibration data with maintenance history on-board card setand having a remotely organized collector. In embodiments, a datacollection and processing system is provided having storage ofcalibration data with maintenance history on-board card set and having aself-organizing storage for a multi-sensor data collector. Inembodiments, a data collection and processing system is provided havingstorage of calibration data with maintenance history on-board card setand having a self-organizing network coding for multi-sensor datanetwork. In embodiments, a data collection and processing system isprovided having storage of calibration data with maintenance historyon-board card set and having a wearable haptic user interface for anindustrial sensor data collector, with vibration, heat, electrical,and/or sound outputs. In embodiments, a data collection and processingsystem is provided having storage of calibration data with maintenancehistory on-board card set and having heat maps displaying collected datafor AR/VR. In embodiments, a data collection and processing system isprovided having storage of calibration data with maintenance historyon-board card set and having automatically tuned AR/VR visualization ofdata collected by a data collector.

In embodiments, a data collection and processing system is providedhaving a rapid route creation capability using hierarchical templates.In embodiments, a data collection and processing system is providedhaving a rapid route creation capability using hierarchical templatesand having intelligent management of data collection bands. Inembodiments, a data collection and processing system is provided havinga rapid route creation capability using hierarchical templates andhaving a neural net expert system using intelligent management of datacollection bands. In embodiments, a data collection and processingsystem is provided having a rapid route creation capability usinghierarchical templates and having use of a database hierarchy in sensordata analysis. In embodiments, a data collection and processing systemis provided having a rapid route creation capability using hierarchicaltemplates and having an expert system GUI graphical approach to definingintelligent data collection bands and diagnoses for the expert system.In embodiments, a data collection and processing system is providedhaving a rapid route creation capability using hierarchical templatesand having a graphical approach for back-calculation definition. Inembodiments, a data collection and processing system is provided havinga rapid route creation capability using hierarchical templates andhaving proposed bearing analysis methods. In embodiments, a datacollection and processing system is provided having a rapid routecreation capability using hierarchical templates and having torsionalvibration detection/analysis utilizing transitory signal analysis. Inembodiments, a data collection and processing system is provided havinga rapid route creation capability using hierarchical templates andhaving improved integration using both analog and digital methods. Inembodiments, a data collection and processing system is provided havinga rapid route creation capability using hierarchical templates andhaving adaptive scheduling techniques for continuous monitoring ofanalog data in a local environment. In embodiments, a data collectionand processing system is provided having a rapid route creationcapability using hierarchical templates and having data acquisitionparking features. In embodiments, a data collection and processingsystem is provided having a rapid route creation capability usinghierarchical templates and having a self-sufficient data acquisitionbox. In embodiments, a data collection and processing system is providedhaving a rapid route creation capability using hierarchical templatesand having SD card storage. In embodiments, a data collection andprocessing system is provided having a rapid route creation capabilityusing hierarchical templates and having extended onboard statisticalcapabilities for continuous monitoring. In embodiments, a datacollection and processing system is provided having a rapid routecreation capability using hierarchical templates and having the use ofambient, local and vibration noise for prediction. In embodiments, adata collection and processing system is provided having a rapid routecreation capability using hierarchical templates and having smart routechanges route based on incoming data or alarms to enable simultaneousdynamic data for analysis or correlation. In embodiments, a datacollection and processing system is provided having a rapid routecreation capability using hierarchical templates and having smart ODSand transfer functions. In embodiments, a data collection and processingsystem is provided having a rapid route creation capability usinghierarchical templates and having a hierarchical multiplexer. Inembodiments, a data collection and processing system is provided havinga rapid route creation capability using hierarchical templates andhaving identification of sensor overload. In embodiments, a datacollection and processing system is provided having a rapid routecreation capability using hierarchical templates and having RFidentification and an inclinometer. In embodiments, a data collectionand processing system is provided having a rapid route creationcapability using hierarchical templates and having continuous ultrasonicmonitoring. In embodiments, a data collection and processing system isprovided having a rapid route creation capability using hierarchicaltemplates and having cloud-based, machine pattern recognition based onfusion of remote, analog industrial sensors. In embodiments, a datacollection and processing system is provided having a rapid routecreation capability using hierarchical templates and having cloud-based,machine pattern analysis of state information from multiple analogindustrial sensors to provide anticipated state information for anindustrial system. In embodiments, a data collection and processingsystem is provided having a rapid route creation capability usinghierarchical templates and having cloud-based policy automation enginefor IoT, with creation, deployment, and management of IoT devices. Inembodiments, a data collection and processing system is provided havinga rapid route creation capability using hierarchical templates andhaving on-device sensor fusion and data storage for industrial IoTdevices. In embodiments, a data collection and processing system isprovided having a rapid route creation capability using hierarchicaltemplates and having a self-organizing data marketplace for industrialIoT data. In embodiments, a data collection and processing system isprovided having a rapid route creation capability using hierarchicaltemplates and having self-organization of data pools based onutilization and/or yield metrics. In embodiments, a data collection andprocessing system is provided having a rapid route creation capabilityusing hierarchical templates and having training AI models based onindustry-specific feedback. In embodiments, a data collection andprocessing system is provided having a rapid route creation capabilityusing hierarchical templates and having a self-organized swarm ofindustrial data collectors. In embodiments, a data collection andprocessing system is provided having a rapid route creation capabilityusing hierarchical templates and having an IoT distributed ledger. Inembodiments, a data collection and processing system is provided havinga rapid route creation capability using hierarchical templates andhaving a self-organizing collector. In embodiments, a data collectionand processing system is provided having a rapid route creationcapability using hierarchical templates and having a network-sensitivecollector. In embodiments, a data collection and processing system isprovided having a rapid route creation capability using hierarchicaltemplates and having a remotely organized collector. In embodiments, adata collection and processing system is provided having a rapid routecreation capability using hierarchical templates and having aself-organizing storage for a multi-sensor data collector. Inembodiments, a data collection and processing system is provided havinga rapid route creation capability using hierarchical templates andhaving a self-organizing network coding for multi-sensor data network.In embodiments, a data collection and processing system is providedhaving a rapid route creation capability using hierarchical templatesand having a wearable haptic user interface for an industrial sensordata collector, with vibration, heat, electrical and/or sound outputs.In embodiments, a data collection and processing system is providedhaving a rapid route creation capability using hierarchical templatesand having heat maps displaying collected data for AR/VR. Inembodiments, a data collection and processing system is provided havinga rapid route creation capability using hierarchical templates andhaving automatically tuned AR/VR visualization of data collected by adata collector.

In embodiments, a data collection and processing system is providedhaving intelligent management of data collection bands. In embodiments,a data collection and processing system is provided having intelligentmanagement of data collection bands and having a neural net expertsystem using intelligent management of data collection bands. Inembodiments, a data collection and processing system is provided havingintelligent management of data collection bands and having use of adatabase hierarchy in sensor data analysis. In embodiments, a datacollection and processing system is provided having intelligentmanagement of data collection bands and having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system. In embodiments, a data collection andprocessing system is provided having intelligent management of datacollection bands and having a graphical approach for back-calculationdefinition. In embodiments, a data collection and processing system isprovided having intelligent management of data collection bands andhaving proposed bearing analysis methods. In embodiments, a datacollection and processing system is provided having intelligentmanagement of data collection bands and having torsional vibrationdetection/analysis utilizing transitory signal analysis. In embodiments,a data collection and processing system is provided having intelligentmanagement of data collection bands and having improved integrationusing both analog and digital methods. In embodiments, a data collectionand processing system is provided having intelligent management of datacollection bands and having adaptive scheduling techniques forcontinuous monitoring of analog data in a local environment. Inembodiments, a data collection and processing system is provided havingintelligent management of data collection bands and having dataacquisition parking features. In embodiments, a data collection andprocessing system is provided having intelligent management of datacollection bands and having a self-sufficient data acquisition box. Inembodiments, a data collection and processing system is provided havingintelligent management of data collection bands and having SD cardstorage. In embodiments, a data collection and processing system isprovided having intelligent management of data collection bands andhaving extended onboard statistical capabilities for continuousmonitoring. In embodiments, a data collection and processing system isprovided having intelligent management of data collection bands andhaving the use of ambient, local and vibration noise for prediction. Inembodiments, a data collection and processing system is provided havingintelligent management of data collection bands and having smart routechanges route based on incoming data or alarms to enable simultaneousdynamic data for analysis or correlation. In embodiments, a datacollection and processing system is provided having intelligentmanagement of data collection bands and having smart ODS and transferfunctions. In embodiments, a data collection and processing system isprovided having intelligent management of data collection bands andhaving a hierarchical multiplexer. In embodiments, a data collection andprocessing system is provided having intelligent management of datacollection bands and having identification of sensor overload. Inembodiments, a data collection and processing system is provided havingintelligent management of data collection bands and having RFidentification and an inclinometer. In embodiments, a data collectionand processing system is provided having intelligent management of datacollection bands and having continuous ultrasonic monitoring. Inembodiments, a data collection and processing system is provided havingintelligent management of data collection bands and having cloud-based,machine pattern recognition based on fusion of remote, analog industrialsensors. In embodiments, a data collection and processing system isprovided having intelligent management of data collection bands andhaving cloud-based, machine pattern analysis of state information frommultiple analog industrial sensors to provide anticipated stateinformation for an industrial system. In embodiments, a data collectionand processing system is provided having intelligent management of datacollection bands and having cloud-based policy automation engine forIoT, with creation, deployment, and management of IoT devices. Inembodiments, a data collection and processing system is provided havingintelligent management of data collection bands and having on-devicesensor fusion and data storage for industrial IoT devices. Inembodiments, a data collection and processing system is provided havingintelligent management of data collection bands and having aself-organizing data marketplace for industrial IoT data. Inembodiments, a data collection and processing system is provided havingintelligent management of data collection bands and havingself-organization of data pools based on utilization and/or yieldmetrics. In embodiments, a data collection and processing system isprovided having intelligent management of data collection bands andhaving training AI models based on industry-specific feedback. Inembodiments, a data collection and processing system is provided havingintelligent management of data collection bands and having aself-organized swarm of industrial data collectors. In embodiments, adata collection and processing system is provided having intelligentmanagement of data collection bands and having an IoT distributedledger. In embodiments, a data collection and processing system isprovided having intelligent management of data collection bands andhaving a self-organizing collector. In embodiments, a data collectionand processing system is provided having intelligent management of datacollection bands and having a network-sensitive collector. Inembodiments, a data collection and processing system is provided havingintelligent management of data collection bands and having a remotelyorganized collector. In embodiments, a data collection and processingsystem is provided having intelligent management of data collectionbands and having a self-organizing storage for a multi-sensor datacollector. In embodiments, a data collection and processing system isprovided having intelligent management of data collection bands andhaving a self-organizing network coding for multi-sensor data network.In embodiments, a data collection and processing system is providedhaving intelligent management of data collection bands and having awearable haptic user interface for an industrial sensor data collector,with vibration, heat, electrical, and/or sound outputs. In embodiments,a data collection and processing system is provided having intelligentmanagement of data collection bands and having heat maps displayingcollected data for AR/VR. In embodiments, a data collection andprocessing system is provided having intelligent management of datacollection bands and having automatically tuned AR/VR visualization ofdata collected by a data collector.

In embodiments, a data collection and processing system is providedhaving a neural net expert system using intelligent management of datacollection bands. In embodiments, a data collection and processingsystem is provided having a neural net expert system using intelligentmanagement of data collection bands and having use of a databasehierarchy in sensor data analysis. In embodiments, a data collection andprocessing system is provided having a neural net expert system usingintelligent management of data collection bands and having an expertsystem GUI graphical approach to defining intelligent data collectionbands and diagnoses for the expert system. In embodiments, a datacollection and processing system is provided having a neural net expertsystem using intelligent management of data collection bands and havinga graphical approach for back-calculation definition. In embodiments, adata collection and processing system is provided having a neural netexpert system using intelligent management of data collection bands andhaving proposed bearing analysis methods. In embodiments, a datacollection and processing system is provided having a neural net expertsystem using intelligent management of data collection bands and havingtorsional vibration detection/analysis utilizing transitory signalanalysis. In embodiments, a data collection and processing system isprovided having a neural net expert system using intelligent managementof data collection bands and having improved integration using bothanalog and digital methods. In embodiments, a data collection andprocessing system is provided having a neural net expert system usingintelligent management of data collection bands and having adaptivescheduling techniques for continuous monitoring of analog data in alocal environment. In embodiments, a data collection and processingsystem is provided having a neural net expert system using intelligentmanagement of data collection bands and having data acquisition parkingfeatures. In embodiments, a data collection and processing system isprovided having a neural net expert system using intelligent managementof data collection bands and having a self-sufficient data acquisitionbox. In embodiments, a data collection and processing system is providedhaving a neural net expert system using intelligent management of datacollection bands and having SD card storage. In embodiments, a datacollection and processing system is provided having a neural net expertsystem using intelligent management of data collection bands and havingextended onboard statistical capabilities for continuous monitoring. Inembodiments, a data collection and processing system is provided havinga neural net expert system using intelligent management of datacollection bands and having the use of ambient, local and vibrationnoise for prediction. In embodiments, a data collection and processingsystem is provided having a neural net expert system using intelligentmanagement of data collection bands and having smart route changes routebased on incoming data or alarms to enable simultaneous dynamic data foranalysis or correlation. In embodiments, a data collection andprocessing system is provided having a neural net expert system usingintelligent management of data collection bands and having smart ODS andtransfer functions. In embodiments, a data collection and processingsystem is provided having a neural net expert system using intelligentmanagement of data collection bands and having a hierarchicalmultiplexer. In embodiments, a data collection and processing system isprovided having a neural net expert system using intelligent managementof data collection bands and having identification of sensor overload.In embodiments, a data collection and processing system is providedhaving a neural net expert system using intelligent management of datacollection bands and having RF identification and an inclinometer. Inembodiments, a data collection and processing system is provided havinga neural net expert system using intelligent management of datacollection bands and having continuous ultrasonic monitoring. Inembodiments, a data collection and processing system is provided havinga neural net expert system using intelligent management of datacollection bands and having cloud-based, machine pattern recognitionbased on fusion of remote, analog industrial sensors. In embodiments, adata collection and processing system is provided having a neural netexpert system using intelligent management of data collection bands andhaving cloud-based, machine pattern analysis of state information frommultiple analog industrial sensors to provide anticipated stateinformation for an industrial system. In embodiments, a data collectionand processing system is provided having a neural net expert systemusing intelligent management of data collection bands and havingcloud-based policy automation engine for IoT, with creation, deployment,and management of IoT devices. In embodiments, a data collection andprocessing system is provided having a neural net expert system usingintelligent management of data collection bands and having on-devicesensor fusion and data storage for industrial IoT devices. Inembodiments, a data collection and processing system is provided havinga neural net expert system using intelligent management of datacollection bands and having a self-organizing data marketplace forindustrial IoT data. In embodiments, a data collection and processingsystem is provided having a neural net expert system using intelligentmanagement of data collection bands and having self-organization of datapools based on utilization and/or yield metrics. In embodiments, a datacollection and processing system is provided having a neural net expertsystem using intelligent management of data collection bands and havingtraining AI models based on industry-specific feedback. In embodiments,a data collection and processing system is provided having a neural netexpert system using intelligent management of data collection bands andhaving a self-organized swarm of industrial data collectors. Inembodiments, a data collection and processing system is provided havinga neural net expert system using intelligent management of datacollection bands and having an IoT distributed ledger. In embodiments, adata collection and processing system is provided having a neural netexpert system using intelligent management of data collection bands andhaving a self-organizing collector. In embodiments, a data collectionand processing system is provided having a neural net expert systemusing intelligent management of data collection bands and having anetwork-sensitive collector. In embodiments, a data collection andprocessing system is provided having a neural net expert system usingintelligent management of data collection bands and having a remotelyorganized collector. In embodiments, a data collection and processingsystem is provided having a neural net expert system using intelligentmanagement of data collection bands and having a self-organizing storagefor a multi-sensor data collector. In embodiments, a data collection andprocessing system is provided having a neural net expert system usingintelligent management of data collection bands and having aself-organizing network coding for multi-sensor data network. Inembodiments, a data collection and processing system is provided havinga neural net expert system using intelligent management of datacollection bands and having a wearable haptic user interface for anindustrial sensor data collector, with vibration, heat, electricaland/or sound outputs. In embodiments, a data collection and processingsystem is provided having a neural net expert system using intelligentmanagement of data collection bands and having heat maps displayingcollected data for AR/VR. In embodiments, a data collection andprocessing system is provided having a neural net expert system usingintelligent management of data collection bands and having automaticallytuned AR/VR visualization of data collected by a data collector.

In embodiments, a data collection and processing system is providedhaving use of a database hierarchy in sensor data analysis. Inembodiments, a data collection and processing system is provided havinguse of a database hierarchy in sensor data analysis and having an expertsystem GUI graphical approach to defining intelligent data collectionbands and diagnoses for the expert system. In embodiments, a datacollection and processing system is provided having use of a databasehierarchy in sensor data analysis and having a graphical approach forback-calculation definition. In embodiments, a data collection andprocessing system is provided having use of a database hierarchy insensor data analysis and having proposed bearing analysis methods. Inembodiments, a data collection and processing system is provided havinguse of a database hierarchy in sensor data analysis and having torsionalvibration detection/analysis utilizing transitory signal analysis. Inembodiments, a data collection and processing system is provided havinguse of a database hierarchy in sensor data analysis and having improvedintegration using both analog and digital methods. In embodiments, adata collection and processing system is provided having use of adatabase hierarchy in sensor data analysis and having adaptivescheduling techniques for continuous monitoring of analog data in alocal environment. In embodiments, a data collection and processingsystem is provided having use of a database hierarchy in sensor dataanalysis and having data acquisition parking features. In embodiments, adata collection and processing system is provided having use of adatabase hierarchy in sensor data analysis and having a self-sufficientdata acquisition box. In embodiments, a data collection and processingsystem is provided having use of a database hierarchy in sensor dataanalysis and having SD card storage. In embodiments, a data collectionand processing system is provided having use of a database hierarchy insensor data analysis and having extended onboard statisticalcapabilities for continuous monitoring. In embodiments, a datacollection and processing system is provided having use of a databasehierarchy in sensor data analysis and having the use of ambient, localand vibration noise for prediction. In embodiments, a data collectionand processing system is provided having use of a database hierarchy insensor data analysis and having smart route changes route based onincoming data or alarms to enable simultaneous dynamic data for analysisor correlation. In embodiments, a data collection and processing systemis provided having use of a database hierarchy in sensor data analysisand having smart ODS and transfer functions. In embodiments, a datacollection and processing system is provided having use of a databasehierarchy in sensor data analysis and having a hierarchical multiplexer.In embodiments, a data collection and processing system is providedhaving use of a database hierarchy in sensor data analysis and havingidentification of sensor overload. In embodiments, a data collection andprocessing system is provided having use of a database hierarchy insensor data analysis and having RF identification and an inclinometer.In embodiments, a data collection and processing system is providedhaving use of a database hierarchy in sensor data analysis and havingcontinuous ultrasonic monitoring. In embodiments, a data collection andprocessing system is provided having use of a database hierarchy insensor data analysis and having cloud-based, machine pattern recognitionbased on fusion of remote, analog industrial sensors. In embodiments, adata collection and processing system is provided having use of adatabase hierarchy in sensor data analysis and having cloud-based,machine pattern analysis of state information from multiple analogindustrial sensors to provide anticipated state information for anindustrial system. In embodiments, a data collection and processingsystem is provided having use of a database hierarchy in sensor dataanalysis and having cloud-based policy automation engine for IoT, withcreation, deployment, and management of IoT devices. In embodiments, adata collection and processing system is provided having use of adatabase hierarchy in sensor data analysis and having on-device sensorfusion and data storage for industrial IoT devices. In embodiments, adata collection and processing system is provided having use of adatabase hierarchy in sensor data analysis and having a self-organizingdata marketplace for industrial IoT data. In embodiments, a datacollection and processing system is provided having use of a databasehierarchy in sensor data analysis and having self-organization of datapools based on utilization and/or yield metrics. In embodiments, a datacollection and processing system is provided having use of a databasehierarchy in sensor data analysis and having training AI models based onindustry-specific feedback. In embodiments, a data collection andprocessing system is provided having use of a database hierarchy insensor data analysis and having a self-organized swarm of industrialdata collectors. In embodiments, a data collection and processing systemis provided having use of a database hierarchy in sensor data analysisand having an IoT distributed ledger. In embodiments, a data collectionand processing system is provided having use of a database hierarchy insensor data analysis and having a self-organizing collector. Inembodiments, a data collection and processing system is provided havinguse of a database hierarchy in sensor data analysis and having anetwork-sensitive collector. In embodiments, a data collection andprocessing system is provided having use of a database hierarchy insensor data analysis and having a remotely organized collector. Inembodiments, a data collection and processing system is provided havinguse of a database hierarchy in sensor data analysis and having aself-organizing storage for a multi-sensor data collector. Inembodiments, a data collection and processing system is provided havinguse of a database hierarchy in sensor data analysis and having aself-organizing network coding for multi-sensor data network. Inembodiments, a data collection and processing system is provided havinguse of a database hierarchy in sensor data analysis and having awearable haptic user interface for an industrial sensor data collector,with vibration, heat, electrical, and/or sound outputs. In embodiments,a data collection and processing system is provided having use of adatabase hierarchy in sensor data analysis and having heat mapsdisplaying collected data for AR/VR. In embodiments, a data collectionand processing system is provided having use of a database hierarchy insensor data analysis and having automatically tuned AR/VR visualizationof data collected by a data collector.

In embodiments, a data collection and processing system is providedhaving an expert system GUI graphical approach to defining intelligentdata collection bands and diagnoses for the expert system. Inembodiments, a data collection and processing system is provided havingan expert system GUI graphical approach to defining intelligent datacollection bands and diagnoses for the expert system and having agraphical approach for back-calculation definition. In embodiments, adata collection and processing system is provided having an expertsystem GUI graphical approach to defining intelligent data collectionbands and diagnoses for the expert system and having proposed bearinganalysis methods. In embodiments, a data collection and processingsystem is provided having an expert system GUI graphical approach todefining intelligent data collection bands and diagnoses for the expertsystem and having torsional vibration detection/analysis utilizingtransitory signal analysis. In embodiments, a data collection andprocessing system is provided having an expert system GUI graphicalapproach to defining intelligent data collection bands and diagnoses forthe expert system and having improved integration using both analog anddigital methods. In embodiments, a data collection and processing systemis provided having an expert system GUI graphical approach to definingintelligent data collection bands and diagnoses for the expert systemand having adaptive scheduling techniques for continuous monitoring ofanalog data in a local environment. In embodiments, a data collectionand processing system is provided having an expert system GUI graphicalapproach to defining intelligent data collection bands and diagnoses forthe expert system and having data acquisition parking features. Inembodiments, a data collection and processing system is provided havingan expert system GUI graphical approach to defining intelligent datacollection bands and diagnoses for the expert system and having aself-sufficient data acquisition box. In embodiments, a data collectionand processing system is provided having an expert system GUI graphicalapproach to defining intelligent data collection bands and diagnoses forthe expert system and having SD card storage. In embodiments, a datacollection and processing system is provided having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system and having extended onboard statisticalcapabilities for continuous monitoring. In embodiments, a datacollection and processing system is provided having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system and having the use of ambient, local andvibration noise for prediction. In embodiments, a data collection andprocessing system is provided having an expert system GUI graphicalapproach to defining intelligent data collection bands and diagnoses forthe expert system and having smart route changes route based on incomingdata or alarms to enable simultaneous dynamic data for analysis orcorrelation. In embodiments, a data collection and processing system isprovided having an expert system GUI graphical approach to definingintelligent data collection bands and diagnoses for the expert systemand having smart ODS and transfer functions. In embodiments, a datacollection and processing system is provided having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system and having a hierarchical multiplexer.In embodiments, a data collection and processing system is providedhaving an expert system GUI graphical approach to defining intelligentdata collection bands and diagnoses for the expert system and havingidentification of sensor overload. In embodiments, a data collection andprocessing system is provided having an expert system GUI graphicalapproach to defining intelligent data collection bands and diagnoses forthe expert system and having RF identification and an inclinometer. Inembodiments, a data collection and processing system is provided havingan expert system GUI graphical approach to defining intelligent datacollection bands and diagnoses for the expert system and havingcontinuous ultrasonic monitoring. In embodiments, a data collection andprocessing system is provided having an expert system GUI graphicalapproach to defining intelligent data collection bands and diagnoses forthe expert system and having cloud-based, machine pattern recognitionbased on fusion of remote, analog industrial sensors. In embodiments, adata collection and processing system is provided having an expertsystem GUI graphical approach to defining intelligent data collectionbands and diagnoses for the expert system and having cloud-based,machine pattern analysis of state information from multiple analogindustrial sensors to provide anticipated state information for anindustrial system. In embodiments, a data collection and processingsystem is provided having an expert system GUI graphical approach todefining intelligent data collection bands and diagnoses for the expertsystem and having cloud-based policy automation engine for IoT, withcreation, deployment, and management of IoT devices. In embodiments, adata collection and processing system is provided having an expertsystem GUI graphical approach to defining intelligent data collectionbands and diagnoses for the expert system and having on-device sensorfusion and data storage for industrial IoT devices. In embodiments, adata collection and processing system is provided having an expertsystem GUI graphical approach to defining intelligent data collectionbands and diagnoses for the expert system and having a self-organizingdata marketplace for industrial IoT data. In embodiments, a datacollection and processing system is provided having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system and having self-organization of datapools based on utilization and/or yield metrics. In embodiments, a datacollection and processing system is provided having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system and having training AI models based onindustry-specific feedback. In embodiments, a data collection andprocessing system is provided having an expert system GUI graphicalapproach to defining intelligent data collection bands and diagnoses forthe expert system and having a self-organized swarm of industrial datacollectors. In embodiments, a data collection and processing system isprovided having an expert system GUI graphical approach to definingintelligent data collection bands and diagnoses for the expert systemand having an IoT distributed ledger. In embodiments, a data collectionand processing system is provided having an expert system GUI graphicalapproach to defining intelligent data collection bands and diagnoses forthe expert system and having a self-organizing collector. Inembodiments, a data collection and processing system is provided havingan expert system GUI graphical approach to defining intelligent datacollection bands and diagnoses for the expert system and having anetwork-sensitive collector. In embodiments, a data collection andprocessing system is provided having an expert system GUI graphicalapproach to defining intelligent data collection bands and diagnoses forthe expert system and having a remotely organized collector. Inembodiments, a data collection and processing system is provided havingan expert system GUI graphical approach to defining intelligent datacollection bands and diagnoses for the expert system and having aself-organizing storage for a multi-sensor data collector. Inembodiments, a data collection and processing system is provided havingan expert system GUI graphical approach to defining intelligent datacollection bands and diagnoses for the expert system and having aself-organizing network coding for multi-sensor data network. Inembodiments, a data collection and processing system is provided havingan expert system GUI graphical approach to defining intelligent datacollection bands and diagnoses for the expert system and having awearable haptic user interface for an industrial sensor data collector,with vibration, heat, electrical and/or sound outputs. In embodiments, adata collection and processing system is provided having an expertsystem GUI graphical approach to defining intelligent data collectionbands and diagnoses for the expert system and having heat mapsdisplaying collected data for AR/VR. In embodiments, a data collectionand processing system is provided having an expert system GUI graphicalapproach to defining intelligent data collection bands and diagnoses forthe expert system and having automatically tuned AR/VR visualization ofdata collected by a data collector.

In embodiments, a data collection and processing system is providedhaving a graphical approach for back-calculation definition. Inembodiments, a data collection and processing system is provided havinga graphical approach for back-calculation definition and having proposedbearing analysis methods. In embodiments, a data collection andprocessing system is provided having a graphical approach forback-calculation definition and having torsional vibrationdetection/analysis utilizing transitory signal analysis. In embodiments,a data collection and processing system is provided having a graphicalapproach for back-calculation definition and having improved integrationusing both analog and digital methods. In embodiments, a data collectionand processing system is provided having a graphical approach forback-calculation definition and having adaptive scheduling techniquesfor continuous monitoring of analog data in a local environment. Inembodiments, a data collection and processing system is provided havinga graphical approach for back-calculation definition and having dataacquisition parking features. In embodiments, a data collection andprocessing system is provided having a graphical approach forback-calculation definition and having a self-sufficient dataacquisition box. In embodiments, a data collection and processing systemis provided having a graphical approach for back-calculation definitionand having SD card storage. In embodiments, a data collection andprocessing system is provided having a graphical approach forback-calculation definition and having extended onboard statisticalcapabilities for continuous monitoring. In embodiments, a datacollection and processing system is provided having a graphical approachfor back-calculation definition and having the use of ambient, local andvibration noise for prediction. In embodiments, a data collection andprocessing system is provided having a graphical approach forback-calculation definition and having smart route changes route basedon incoming data or alarms to enable simultaneous dynamic data foranalysis or correlation. In embodiments, a data collection andprocessing system is provided having a graphical approach forback-calculation definition and having smart ODS and transfer functions.In embodiments, a data collection and processing system is providedhaving a graphical approach for back-calculation definition and having ahierarchical multiplexer. In embodiments, a data collection andprocessing system is provided having a graphical approach forback-calculation definition and having identification of sensoroverload. In embodiments, a data collection and processing system isprovided having a graphical approach for back-calculation definition andhaving RF identification and an inclinometer. In embodiments, a datacollection and processing system is provided having a graphical approachfor back-calculation definition and having continuous ultrasonicmonitoring. In embodiments, a data collection and processing system isprovided having a graphical approach for back-calculation definition andhaving cloud-based, machine pattern recognition based on fusion ofremote, analog industrial sensors. In embodiments, a data collection andprocessing system is provided having a graphical approach forback-calculation definition and having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system. Inembodiments, a data collection and processing system is provided havinga graphical approach for back-calculation definition and havingcloud-based policy automation engine for IoT, with creation, deployment,and management of IoT devices. In embodiments, a data collection andprocessing system is provided having a graphical approach forback-calculation definition and having on-device sensor fusion and datastorage for industrial IoT devices. In embodiments, a data collectionand processing system is provided having a graphical approach forback-calculation definition and having a self-organizing datamarketplace for industrial IoT data. In embodiments, a data collectionand processing system is provided having a graphical approach forback-calculation definition and having self-organization of data poolsbased on utilization and/or yield metrics. In embodiments, a datacollection and processing system is provided having a graphical approachfor back-calculation definition and having training AI models based onindustry-specific feedback. In embodiments, a data collection andprocessing system is provided having a graphical approach forback-calculation definition and having a self-organized swarm ofindustrial data collectors. In embodiments, a data collection andprocessing system is provided having a graphical approach forback-calculation definition and having an IoT distributed ledger. Inembodiments, a data collection and processing system is provided havinga graphical approach for back-calculation definition and having aself-organizing collector. In embodiments, a data collection andprocessing system is provided having a graphical approach forback-calculation definition and having a network-sensitive collector. Inembodiments, a data collection and processing system is provided havinga graphical approach for back-calculation definition and having aremotely organized collector. In embodiments, a data collection andprocessing system is provided having a graphical approach forback-calculation definition and having a self-organizing storage for amulti-sensor data collector. In embodiments, a data collection andprocessing system is provided having a graphical approach forback-calculation definition and having a self-organizing network codingfor multi-sensor data network. In embodiments, a data collection andprocessing system is provided having a graphical approach forback-calculation definition and having a wearable haptic user interfacefor an industrial sensor data collector, with vibration, heat,electrical, and/or sound outputs. In embodiments, a data collection andprocessing system is provided having a graphical approach forback-calculation definition and having heat maps displaying collecteddata for AR/VR. In embodiments, a data collection and processing systemis provided having a graphical approach for back-calculation definitionand having automatically tuned AR/VR visualization of data collected bya data collector.

In embodiments, a data collection and processing system is providedhaving proposed bearing analysis methods. In embodiments, a datacollection and processing system is provided having proposed bearinganalysis methods and having torsional vibration detection/analysisutilizing transitory signal analysis. In embodiments, a data collectionand processing system is provided having proposed bearing analysismethods and having improved integration using both analog and digitalmethods. In embodiments, a data collection and processing system isprovided having proposed bearing analysis methods and having adaptivescheduling techniques for continuous monitoring of analog data in alocal environment. In embodiments, a data collection and processingsystem is provided having proposed bearing analysis methods and havingdata acquisition parking features. In embodiments, a data collection andprocessing system is provided having proposed bearing analysis methodsand having a self-sufficient data acquisition box. In embodiments, adata collection and processing system is provided having proposedbearing analysis methods and having SD card storage. In embodiments, adata collection and processing system is provided having proposedbearing analysis methods and having extended onboard statisticalcapabilities for continuous monitoring. In embodiments, a datacollection and processing system is provided having proposed bearinganalysis methods and having the use of ambient, local and vibrationnoise for prediction. In embodiments, a data collection and processingsystem is provided having proposed bearing analysis methods and havingsmart route changes route based on incoming data or alarms to enablesimultaneous dynamic data for analysis or correlation. In embodiments, adata collection and processing system is provided having proposedbearing analysis methods and having smart ODS and transfer functions. Inembodiments, a data collection and processing system is provided havingproposed bearing analysis methods and having a hierarchical multiplexer.In embodiments, a data collection and processing system is providedhaving proposed bearing analysis methods and having identification ofsensor overload. In embodiments, a data collection and processing systemis provided having proposed bearing analysis methods and having RFidentification and an inclinometer. In embodiments, a data collectionand processing system is provided having proposed bearing analysismethods and having continuous ultrasonic monitoring. In embodiments, adata collection and processing system is provided having proposedbearing analysis methods and having cloud-based, machine patternrecognition based on fusion of remote, analog industrial sensors. Inembodiments, a data collection and processing system is provided havingproposed bearing analysis methods and having cloud-based, machinepattern analysis of state information from multiple analog industrialsensors to provide anticipated state information for an industrialsystem. In embodiments, a data collection and processing system isprovided having proposed bearing analysis methods and having cloud-basedpolicy automation engine for IoT, with creation, deployment, andmanagement of IoT devices. In embodiments, a data collection andprocessing system is provided having proposed bearing analysis methodsand having on-device sensor fusion and data storage for industrial IoTdevices. In embodiments, a data collection and processing system isprovided having proposed bearing analysis methods and having aself-organizing data marketplace for industrial IoT data. Inembodiments, a data collection and processing system is provided havingproposed bearing analysis methods and having self-organization of datapools based on utilization and/or yield metrics. In embodiments, a datacollection and processing system is provided having proposed bearinganalysis methods and having training AI models based onindustry-specific feedback. In embodiments, a data collection andprocessing system is provided having proposed bearing analysis methodsand having a self-organized swarm of industrial data collectors. Inembodiments, a data collection and processing system is provided havingproposed bearing analysis methods and having an IoT distributed ledger.In embodiments, a data collection and processing system is providedhaving proposed bearing analysis methods and having a self-organizingcollector. In embodiments, a data collection and processing system isprovided having proposed bearing analysis methods and having anetwork-sensitive collector. In embodiments, a data collection andprocessing system is provided having proposed bearing analysis methodsand having a remotely organized collector. In embodiments, a datacollection and processing system is provided having proposed bearinganalysis methods and having a self-organizing storage for a multi-sensordata collector. In embodiments, a data collection and processing systemis provided having proposed bearing analysis methods and having aself-organizing network coding for multi-sensor data network. Inembodiments, a data collection and processing system is provided havingproposed bearing analysis methods and having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs. In embodiments, a data collection andprocessing system is provided having proposed bearing analysis methodsand having heat maps displaying collected data for AR/VR. Inembodiments, a data collection and processing system is provided havingproposed bearing analysis methods and having automatically tuned AR/VRvisualization of data collected by a data collector.

In embodiments, a data collection and processing system is providedhaving torsional vibration detection/analysis utilizing transitorysignal analysis. In embodiments, a data collection and processing systemis provided having torsional vibration detection/analysis utilizingtransitory signal analysis and having improved integration using bothanalog and digital methods. In embodiments, a data collection andprocessing system is provided having torsional vibrationdetection/analysis utilizing transitory signal analysis and havingadaptive scheduling techniques for continuous monitoring of analog datain a local environment. In embodiments, a data collection and processingsystem is provided having torsional vibration detection/analysisutilizing transitory signal analysis and having data acquisition parkingfeatures. In embodiments, a data collection and processing system isprovided having torsional vibration detection/analysis utilizingtransitory signal analysis and having a self-sufficient data acquisitionbox. In embodiments, a data collection and processing system is providedhaving torsional vibration detection/analysis utilizing transitorysignal analysis and having SD card storage. In embodiments, a datacollection and processing system is provided having torsional vibrationdetection/analysis utilizing transitory signal analysis and havingextended onboard statistical capabilities for continuous monitoring. Inembodiments, a data collection and processing system is provided havingtorsional vibration detection/analysis utilizing transitory signalanalysis and having the use of ambient, local and vibration noise forprediction. In embodiments, a data collection and processing system isprovided having torsional vibration detection/analysis utilizingtransitory signal analysis and having smart route changes route based onincoming data or alarms to enable simultaneous dynamic data for analysisor correlation. In embodiments, a data collection and processing systemis provided having torsional vibration detection/analysis utilizingtransitory signal analysis and having smart ODS and transfer functions.In embodiments, a data collection and processing system is providedhaving torsional vibration detection/analysis utilizing transitorysignal analysis and having a hierarchical multiplexer. In embodiments, adata collection and processing system is provided having torsionalvibration detection/analysis utilizing transitory signal analysis andhaving identification of sensor overload. In embodiments, a datacollection and processing system is provided having torsional vibrationdetection/analysis utilizing transitory signal analysis and having RFidentification and an inclinometer. In embodiments, a data collectionand processing system is provided having torsional vibrationdetection/analysis utilizing transitory signal analysis and havingcontinuous ultrasonic monitoring. In embodiments, a data collection andprocessing system is provided having torsional vibrationdetection/analysis utilizing transitory signal analysis and havingcloud-based, machine pattern recognition based on fusion of remote,analog industrial sensors. In embodiments, a data collection andprocessing system is provided having torsional vibrationdetection/analysis utilizing transitory signal analysis and havingcloud-based, machine pattern analysis of state information from multipleanalog industrial sensors to provide anticipated state information foran industrial system. In embodiments, a data collection and processingsystem is provided having torsional vibration detection/analysisutilizing transitory signal analysis and having cloud-based policyautomation engine for IoT, with creation, deployment, and management ofIoT devices. In embodiments, a data collection and processing system isprovided having torsional vibration detection/analysis utilizingtransitory signal analysis and having on-device sensor fusion and datastorage for industrial IoT devices. In embodiments, a data collectionand processing system is provided having torsional vibrationdetection/analysis utilizing transitory signal analysis and having aself-organizing data marketplace for industrial IoT data. Inembodiments, a data collection and processing system is provided havingtorsional vibration detection/analysis utilizing transitory signalanalysis and having self-organization of data pools based on utilizationand/or yield metrics. In embodiments, a data collection and processingsystem is provided having torsional vibration detection/analysisutilizing transitory signal analysis and having training AI models basedon industry-specific feedback. In embodiments, a data collection andprocessing system is provided having torsional vibrationdetection/analysis utilizing transitory signal analysis and having aself-organized swarm of industrial data collectors. In embodiments, adata collection and processing system is provided having torsionalvibration detection/analysis utilizing transitory signal analysis andhaving an IoT distributed ledger. In embodiments, a data collection andprocessing system is provided having torsional vibrationdetection/analysis utilizing transitory signal analysis and having aself-organizing collector. In embodiments, a data collection andprocessing system is provided having torsional vibrationdetection/analysis utilizing transitory signal analysis and having anetwork-sensitive collector. In embodiments, a data collection andprocessing system is provided having torsional vibrationdetection/analysis utilizing transitory signal analysis and having aremotely organized collector. In embodiments, a data collection andprocessing system is provided having torsional vibrationdetection/analysis utilizing transitory signal analysis and having aself-organizing storage for a multi-sensor data collector. Inembodiments, a data collection and processing system is provided havingtorsional vibration detection/analysis utilizing transitory signalanalysis and having a self-organizing network coding for multi-sensordata network. In embodiments, a data collection and processing system isprovided having torsional vibration detection/analysis utilizingtransitory signal analysis and having a wearable haptic user interfacefor an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs. In embodiments, a data collection andprocessing system is provided having torsional vibrationdetection/analysis utilizing transitory signal analysis and having heatmaps displaying collected data for AR/VR. In embodiments, a datacollection and processing system is provided having torsional vibrationdetection/analysis utilizing transitory signal analysis and havingautomatically tuned AR/VR visualization of data collected by a datacollector.

In embodiments, a data collection and processing system is providedhaving improved integration using both analog and digital methods. Inembodiments, a data collection and processing system is provided havingimproved integration using both analog and digital methods and havingadaptive scheduling techniques for continuous monitoring of analog datain a local environment. In embodiments, a data collection and processingsystem is provided having improved integration using both analog anddigital methods and having data acquisition parking features. Inembodiments, a data collection and processing system is provided havingimproved integration using both analog and digital methods and having aself-sufficient data acquisition box. In embodiments, a data collectionand processing system is provided having improved integration using bothanalog and digital methods and having SD card storage. In embodiments, adata collection and processing system is provided having improvedintegration using both analog and digital methods and having extendedonboard statistical capabilities for continuous monitoring. Inembodiments, a data collection and processing system is provided havingimproved integration using both analog and digital methods and havingthe use of ambient, local and vibration noise for prediction. Inembodiments, a data collection and processing system is provided havingimproved integration using both analog and digital methods and havingsmart route changes route based on incoming data or alarms to enablesimultaneous dynamic data for analysis or correlation. In embodiments, adata collection and processing system is provided having improvedintegration using both analog and digital methods and having smart ODSand transfer functions. In embodiments, a data collection and processingsystem is provided having improved integration using both analog anddigital methods and having a hierarchical multiplexer. In embodiments, adata collection and processing system is provided having improvedintegration using both analog and digital methods and havingidentification of sensor overload. In embodiments, a data collection andprocessing system is provided having improved integration using bothanalog and digital methods and having RF identification and aninclinometer. In embodiments, a data collection and processing system isprovided having improved integration using both analog and digitalmethods and having continuous ultrasonic monitoring. In embodiments, adata collection and processing system is provided having improvedintegration using both analog and digital methods and havingcloud-based, machine pattern recognition based on fusion of remote,analog industrial sensors. In embodiments, a data collection andprocessing system is provided having improved integration using bothanalog and digital methods and having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system. Inembodiments, a data collection and processing system is provided havingimproved integration using both analog and digital methods and havingcloud-based policy automation engine for IoT, with creation, deployment,and management of IoT devices. In embodiments, a data collection andprocessing system is provided having improved integration using bothanalog and digital methods and having on-device sensor fusion and datastorage for industrial IoT devices. In embodiments, a data collectionand processing system is provided having improved integration using bothanalog and digital methods and having a self-organizing data marketplacefor industrial IoT data. In embodiments, a data collection andprocessing system is provided having improved integration using bothanalog and digital methods and having self-organization of data poolsbased on utilization and/or yield metrics. In embodiments, a datacollection and processing system is provided having improved integrationusing both analog and digital methods and having training AI modelsbased on industry-specific feedback. In embodiments, a data collectionand processing system is provided having improved integration using bothanalog and digital methods and having a self-organized swarm ofindustrial data collectors. In embodiments, a data collection andprocessing system is provided having improved integration using bothanalog and digital methods and having an IoT distributed ledger. Inembodiments, a data collection and processing system is provided havingimproved integration using both analog and digital methods and having aself-organizing collector. In embodiments, a data collection andprocessing system is provided having improved integration using bothanalog and digital methods and having a network-sensitive collector. Inembodiments, a data collection and processing system is provided havingimproved integration using both analog and digital methods and having aremotely organized collector. In embodiments, a data collection andprocessing system is provided having improved integration using bothanalog and digital methods and having a self-organizing storage for amulti-sensor data collector. In embodiments, a data collection andprocessing system is provided having improved integration using bothanalog and digital methods and having a self-organizing network codingfor multi-sensor data network. In embodiments, a data collection andprocessing system is provided having improved integration using bothanalog and digital methods and having a wearable haptic user interfacefor an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs. In embodiments, a data collection andprocessing system is provided having improved integration using bothanalog and digital methods and having heat maps displaying collecteddata for AR/VR. In embodiments, a data collection and processing systemis provided having improved integration using both analog and digitalmethods and having automatically tuned AR/VR visualization of datacollected by a data collector.

In embodiments, a data collection and processing system is providedhaving adaptive scheduling techniques for continuous monitoring ofanalog data in a local environment. In embodiments, a data collectionand processing system is provided having adaptive scheduling techniquesfor continuous monitoring of analog data in a local environment andhaving data acquisition parking features. In embodiments, a datacollection and processing system is provided having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment and having a self-sufficient data acquisition box. Inembodiments, a data collection and processing system is provided havingadaptive scheduling techniques for continuous monitoring of analog datain a local environment and having SD card storage. In embodiments, adata collection and processing system is provided having adaptivescheduling techniques for continuous monitoring of analog data in alocal environment and having extended onboard statistical capabilitiesfor continuous monitoring. In embodiments, a data collection andprocessing system is provided having adaptive scheduling techniques forcontinuous monitoring of analog data in a local environment and havingthe use of ambient, local and vibration noise for prediction. Inembodiments, a data collection and processing system is provided havingadaptive scheduling techniques for continuous monitoring of analog datain a local environment and having smart route changes route based onincoming data or alarms to enable simultaneous dynamic data for analysisor correlation. In embodiments, a data collection and processing systemis provided having adaptive scheduling techniques for continuousmonitoring of analog data in a local environment and having smart ODSand transfer functions. In embodiments, a data collection and processingsystem is provided having adaptive scheduling techniques for continuousmonitoring of analog data in a local environment and having ahierarchical multiplexer. In embodiments, a data collection andprocessing system is provided having adaptive scheduling techniques forcontinuous monitoring of analog data in a local environment and havingidentification of sensor overload. In embodiments, a data collection andprocessing system is provided having adaptive scheduling techniques forcontinuous monitoring of analog data in a local environment and havingRF identification and an inclinometer. In embodiments, a data collectionand processing system is provided having adaptive scheduling techniquesfor continuous monitoring of analog data in a local environment andhaving continuous ultrasonic monitoring. In embodiments, a datacollection and processing system is provided having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment and having cloud-based, machine pattern recognition based onfusion of remote, analog industrial sensors. In embodiments, a datacollection and processing system is provided having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment and having cloud-based, machine pattern analysis of stateinformation from multiple analog industrial sensors to provideanticipated state information for an industrial system. In embodiments,a data collection and processing system is provided having adaptivescheduling techniques for continuous monitoring of analog data in alocal environment and having cloud-based policy automation engine forIoT, with creation, deployment, and management of IoT devices. Inembodiments, a data collection and processing system is provided havingadaptive scheduling techniques for continuous monitoring of analog datain a local environment and having on-device sensor fusion and datastorage for industrial IoT devices. In embodiments, a data collectionand processing system is provided having adaptive scheduling techniquesfor continuous monitoring of analog data in a local environment andhaving a self-organizing data marketplace for industrial IoT data. Inembodiments, a data collection and processing system is provided havingadaptive scheduling techniques for continuous monitoring of analog datain a local environment and having self-organization of data pools basedon utilization and/or yield metrics. In embodiments, a data collectionand processing system is provided having adaptive scheduling techniquesfor continuous monitoring of analog data in a local environment andhaving training AI models based on industry-specific feedback. Inembodiments, a data collection and processing system is provided havingadaptive scheduling techniques for continuous monitoring of analog datain a local environment and having a self-organized swarm of industrialdata collectors. In embodiments, a data collection and processing systemis provided having adaptive scheduling techniques for continuousmonitoring of analog data in a local environment and having an IoTdistributed ledger. In embodiments, a data collection and processingsystem is provided having adaptive scheduling techniques for continuousmonitoring of analog data in a local environment and having aself-organizing collector. In embodiments, a data collection andprocessing system is provided having adaptive scheduling techniques forcontinuous monitoring of analog data in a local environment and having anetwork-sensitive collector. In embodiments, a data collection andprocessing system is provided having adaptive scheduling techniques forcontinuous monitoring of analog data in a local environment and having aremotely organized collector. In embodiments, a data collection andprocessing system is provided having adaptive scheduling techniques forcontinuous monitoring of analog data in a local environment and having aself-organizing storage for a multi-sensor data collector. Inembodiments, a data collection and processing system is provided havingadaptive scheduling techniques for continuous monitoring of analog datain a local environment and having a self-organizing network coding formulti-sensor data network. In embodiments, a data collection andprocessing system is provided having adaptive scheduling techniques forcontinuous monitoring of analog data in a local environment and having awearable haptic user interface for an industrial sensor data collector,with vibration, heat, electrical and/or sound outputs. In embodiments, adata collection and processing system is provided having adaptivescheduling techniques for continuous monitoring of analog data in alocal environment and having heat maps displaying collected data forAR/VR. In embodiments, a data collection and processing system isprovided having adaptive scheduling techniques for continuous monitoringof analog data in a local environment and having automatically tunedAR/VR visualization of data collected by a data collector.

In embodiments, a data collection and processing system is providedhaving data acquisition parking features. In embodiments, a datacollection and processing system is provided having data acquisitionparking features and having a self-sufficient data acquisition box. Inembodiments, a data collection and processing system is provided havingdata acquisition parking features and having SD card storage. Inembodiments, a data collection and processing system is provided havingdata acquisition parking features and having extended onboardstatistical capabilities for continuous monitoring. In embodiments, adata collection and processing system is provided having dataacquisition parking features and having the use of ambient, local andvibration noise for prediction. In embodiments, a data collection andprocessing system is provided having data acquisition parking featuresand having smart route changes route based on incoming data or alarms toenable simultaneous dynamic data for analysis or correlation. Inembodiments, a data collection and processing system is provided havingdata acquisition parking features and having smart ODS and transferfunctions. In embodiments, a data collection and processing system isprovided having data acquisition parking features and having ahierarchical multiplexer. In embodiments, a data collection andprocessing system is provided having data acquisition parking featuresand having identification of sensor overload. In embodiments, a datacollection and processing system is provided having data acquisitionparking features and having RF identification and an inclinometer. Inembodiments, a data collection and processing system is provided havingdata acquisition parking features and having continuous ultrasonicmonitoring. In embodiments, a data collection and processing system isprovided having data acquisition parking features and havingcloud-based, machine pattern recognition based on fusion of remote,analog industrial sensors. In embodiments, a data collection andprocessing system is provided having data acquisition parking featuresand having cloud-based, machine pattern analysis of state informationfrom multiple analog industrial sensors to provide anticipated stateinformation for an industrial system. In embodiments, a data collectionand processing system is provided having data acquisition parkingfeatures and having cloud-based policy automation engine for IoT, withcreation, deployment, and management of IoT devices. In embodiments, adata collection and processing system is provided having dataacquisition parking features and having on-device sensor fusion and datastorage for industrial IoT devices. In embodiments, a data collectionand processing system is provided having data acquisition parkingfeatures and having a self-organizing data marketplace for industrialIoT data. In embodiments, a data collection and processing system isprovided having data acquisition parking features and havingself-organization of data pools based on utilization and/or yieldmetrics. In embodiments, a data collection and processing system isprovided having data acquisition parking features and having training AImodels based on industry-specific feedback. In embodiments, a datacollection and processing system is provided having data acquisitionparking features and having a self-organized swarm of industrial datacollectors. In embodiments, a data collection and processing system isprovided having data acquisition parking features and having an IoTdistributed ledger. In embodiments, a data collection and processingsystem is provided having data acquisition parking features and having aself-organizing collector. In embodiments, a data collection andprocessing system is provided having data acquisition parking featuresand having a network-sensitive collector. In embodiments, a datacollection and processing system is provided having data acquisitionparking features and having a remotely organized collector. Inembodiments, a data collection and processing system is provided havingdata acquisition parking features and having a self-organizing storagefor a multi-sensor data collector. In embodiments, a data collection andprocessing system is provided having data acquisition parking featuresand having a self-organizing network coding for multi-sensor datanetwork. In embodiments, a data collection and processing system isprovided having data acquisition parking features and having a wearablehaptic user interface for an industrial sensor data collector, withvibration, heat, electrical and/or sound outputs. In embodiments, a datacollection and processing system is provided having data acquisitionparking features and having heat maps displaying collected data forAR/VR. In embodiments, a data collection and processing system isprovided having data acquisition parking features and havingautomatically tuned AR/VR visualization of data collected by a datacollector.

In embodiments, a data collection and processing system is providedhaving a self-sufficient data acquisition box. In embodiments, a datacollection and processing system is provided having a self-sufficientdata acquisition box and having SD card storage. In embodiments, a datacollection and processing system is provided having a self-sufficientdata acquisition box and having extended onboard statisticalcapabilities for continuous monitoring. In embodiments, a datacollection and processing system is provided having a self-sufficientdata acquisition box and having the use of ambient, local and vibrationnoise for prediction. In embodiments, a data collection and processingsystem is provided having a self-sufficient data acquisition box andhaving smart route changes route based on incoming data or alarms toenable simultaneous dynamic data for analysis or correlation. Inembodiments, a data collection and processing system is provided havinga self-sufficient data acquisition box and having smart ODS and transferfunctions. In embodiments, a data collection and processing system isprovided having a self-sufficient data acquisition box and having ahierarchical multiplexer. In embodiments, a data collection andprocessing system is provided having a self-sufficient data acquisitionbox and having identification of sensor overload. In embodiments, a datacollection and processing system is provided having a self-sufficientdata acquisition box and having RF identification and an inclinometer.In embodiments, a data collection and processing system is providedhaving a self-sufficient data acquisition box and having continuousultrasonic monitoring. In embodiments, a data collection and processingsystem is provided having a self-sufficient data acquisition box andhaving cloud-based, machine pattern recognition based on fusion ofremote, analog industrial sensors. In embodiments, a data collection andprocessing system is provided having a self-sufficient data acquisitionbox and having cloud-based, machine pattern analysis of stateinformation from multiple analog industrial sensors to provideanticipated state information for an industrial system. In embodiments,a data collection and processing system is provided having aself-sufficient data acquisition box and having cloud-based policyautomation engine for IoT, with creation, deployment, and management ofIoT devices. In embodiments, a data collection and processing system isprovided having a self-sufficient data acquisition box and havingon-device sensor fusion and data storage for industrial IoT devices. Inembodiments, a data collection and processing system is provided havinga self-sufficient data acquisition box and having a self-organizing datamarketplace for industrial IoT data. In embodiments, a data collectionand processing system is provided having a self-sufficient dataacquisition box and having self-organization of data pools based onutilization and/or yield metrics. In embodiments, a data collection andprocessing system is provided having a self-sufficient data acquisitionbox and having training AI models based on industry-specific feedback.In embodiments, a data collection and processing system is providedhaving a self-sufficient data acquisition box and having aself-organized swarm of industrial data collectors. In embodiments, adata collection and processing system is provided having aself-sufficient data acquisition box and having an IoT distributedledger. In embodiments, a data collection and processing system isprovided having a self-sufficient data acquisition box and having aself-organizing collector. In embodiments, a data collection andprocessing system is provided having a self-sufficient data acquisitionbox and having a network-sensitive collector. In embodiments, a datacollection and processing system is provided having a self-sufficientdata acquisition box and having a remotely organized collector. Inembodiments, a data collection and processing system is provided havinga self-sufficient data acquisition box and having a self-organizingstorage for a multi-sensor data collector. In embodiments, a datacollection and processing system is provided having a self-sufficientdata acquisition box and having a self-organizing network coding formulti-sensor data network. In embodiments, a data collection andprocessing system is provided having a self-sufficient data acquisitionbox and having a wearable haptic user interface for an industrial sensordata collector, with vibration, heat, electrical, and/or sound outputs.In embodiments, a data collection and processing system is providedhaving a self-sufficient data acquisition box and having heat mapsdisplaying collected data for AR/VR. In embodiments, a data collectionand processing system is provided having a self-sufficient dataacquisition box and having automatically tuned AR/VR visualization ofdata collected by a data collector.

In embodiments, a data collection and processing system is providedhaving SD card storage. In embodiments, a data collection and processingsystem is provided having SD card storage and having extended onboardstatistical capabilities for continuous monitoring. In embodiments, adata collection and processing system is provided having SD card storageand having the use of ambient, local and vibration noise for prediction.In embodiments, a data collection and processing system is providedhaving SD card storage and having smart route changes route based onincoming data or alarms to enable simultaneous dynamic data for analysisor correlation. In embodiments, a data collection and processing systemis provided having SD card storage and having smart ODS and transferfunctions. In embodiments, a data collection and processing system isprovided having SD card storage and having a hierarchical multiplexer.In embodiments, a data collection and processing system is providedhaving SD card storage and having identification of sensor overload. Inembodiments, a data collection and processing system is provided havingSD card storage and having RF identification and an inclinometer. Inembodiments, a data collection and processing system is provided havingSD card storage and having continuous ultrasonic monitoring. Inembodiments, a data collection and processing system is provided havingSD card storage and having cloud-based, machine pattern recognitionbased on fusion of remote, analog industrial sensors. In embodiments, adata collection and processing system is provided having SD card storageand having cloud-based, machine pattern analysis of state informationfrom multiple analog industrial sensors to provide anticipated stateinformation for an industrial system. In embodiments, a data collectionand processing system is provided having SD card storage and havingcloud-based policy automation engine for IoT, with creation, deployment,and management of IoT devices. In embodiments, a data collection andprocessing system is provided having SD card storage and havingon-device sensor fusion and data storage for industrial IoT devices. Inembodiments, a data collection and processing system is provided havingSD card storage and having a self-organizing data marketplace forindustrial IoT data. In embodiments, a data collection and processingsystem is provided having SD card storage and having self-organizationof data pools based on utilization and/or yield metrics. In embodiments,a data collection and processing system is provided having SD cardstorage and having training AI models based on industry-specificfeedback. In embodiments, a data collection and processing system isprovided having SD card storage and having a self-organized swarm ofindustrial data collectors. In embodiments, a data collection andprocessing system is provided having SD card storage and having an IoTdistributed ledger. In embodiments, a data collection and processingsystem is provided having SD card storage and having a self-organizingcollector. In embodiments, a data collection and processing system isprovided having SD card storage and having a network-sensitivecollector. In embodiments, a data collection and processing system isprovided having SD card storage and having a remotely organizedcollector. In embodiments, a data collection and processing system isprovided having SD card storage and having a self-organizing storage fora multi-sensor data collector. In embodiments, a data collection andprocessing system is provided having SD card storage and having aself-organizing network coding for multi-sensor data network. Inembodiments, a data collection and processing system is provided havingSD card storage and having a wearable haptic user interface for anindustrial sensor data collector, with vibration, heat, electrical,and/or sound outputs. In embodiments, a data collection and processingsystem is provided having SD card storage and having heat mapsdisplaying collected data for AR/VR. In embodiments, a data collectionand processing system is provided having SD card storage and havingautomatically tuned AR/VR visualization of data collected by a datacollector.

In embodiments, a data collection and processing system is providedhaving extended onboard statistical capabilities for continuousmonitoring. In embodiments, a data collection and processing system isprovided having extended onboard statistical capabilities for continuousmonitoring and having the use of ambient, local and vibration noise forprediction. In embodiments, a data collection and processing system isprovided having extended onboard statistical capabilities for continuousmonitoring and having smart route changes route based on incoming dataor alarms to enable simultaneous dynamic data for analysis orcorrelation. In embodiments, a data collection and processing system isprovided having extended onboard statistical capabilities for continuousmonitoring and having smart ODS and transfer functions. In embodiments,a data collection and processing system is provided having extendedonboard statistical capabilities for continuous monitoring and having ahierarchical multiplexer. In embodiments, a data collection andprocessing system is provided having extended onboard statisticalcapabilities for continuous monitoring and having identification ofsensor overload. In embodiments, a data collection and processing systemis provided having extended onboard statistical capabilities forcontinuous monitoring and having RF identification and an inclinometer.In embodiments, a data collection and processing system is providedhaving extended onboard statistical capabilities for continuousmonitoring and having continuous ultrasonic monitoring. In embodiments,a data collection and processing system is provided having extendedonboard statistical capabilities for continuous monitoring and havingcloud-based, machine pattern recognition based on fusion of remote,analog industrial sensors. In embodiments, a data collection andprocessing system is provided having extended onboard statisticalcapabilities for continuous monitoring and having cloud-based, machinepattern analysis of state information from multiple analog industrialsensors to provide anticipated state information for an industrialsystem. In embodiments, a data collection and processing system isprovided having extended onboard statistical capabilities for continuousmonitoring and having cloud-based policy automation engine for IoT, withcreation, deployment, and management of IoT devices. In embodiments, adata collection and processing system is provided having extendedonboard statistical capabilities for continuous monitoring and havingon-device sensor fusion and data storage for industrial IoT devices. Inembodiments, a data collection and processing system is provided havingextended onboard statistical capabilities for continuous monitoring andhaving a self-organizing data marketplace for industrial IoT data. Inembodiments, a data collection and processing system is provided havingextended onboard statistical capabilities for continuous monitoring andhaving self-organization of data pools based on utilization and/or yieldmetrics. In embodiments, a data collection and processing system isprovided having extended onboard statistical capabilities for continuousmonitoring and having training AI models based on industry-specificfeedback. In embodiments, a data collection and processing system isprovided having extended onboard statistical capabilities for continuousmonitoring and having a self-organized swarm of industrial datacollectors. In embodiments, a data collection and processing system isprovided having extended onboard statistical capabilities for continuousmonitoring and having an IoT distributed ledger. In embodiments, a datacollection and processing system is provided having extended onboardstatistical capabilities for continuous monitoring and having aself-organizing collector. In embodiments, a data collection andprocessing system is provided having extended onboard statisticalcapabilities for continuous monitoring and having a network-sensitivecollector. In embodiments, a data collection and processing system isprovided having extended onboard statistical capabilities for continuousmonitoring and having a remotely organized collector. In embodiments, adata collection and processing system is provided having extendedonboard statistical capabilities for continuous monitoring and having aself-organizing storage for a multi-sensor data collector. Inembodiments, a data collection and processing system is provided havingextended onboard statistical capabilities for continuous monitoring andhaving a self-organizing network coding for multi-sensor data network.In embodiments, a data collection and processing system is providedhaving extended onboard statistical capabilities for continuousmonitoring and having a wearable haptic user interface for an industrialsensor data collector, with vibration, heat, electrical and/or soundoutputs. In embodiments, a data collection and processing system isprovided having extended onboard statistical capabilities for continuousmonitoring and having heat maps displaying collected data for AR/VR. Inembodiments, a data collection and processing system is provided havingextended onboard statistical capabilities for continuous monitoring andhaving automatically tuned AR/VR visualization of data collected by adata collector.

In embodiments, a data collection and processing system is providedhaving the use of ambient, local and vibration noise for prediction. Inembodiments, a data collection and processing system is provided havingthe use of ambient, local and vibration noise for prediction and havingsmart route changes route based on incoming data or alarms to enablesimultaneous dynamic data for analysis or correlation. In embodiments, adata collection and processing system is provided having the use ofambient, local and vibration noise for prediction and having smart ODSand transfer functions. In embodiments, a data collection and processingsystem is provided having the use of ambient, local and vibration noisefor prediction and having a hierarchical multiplexer. In embodiments, adata collection and processing system is provided having the use ofambient, local and vibration noise for prediction and havingidentification of sensor overload. In embodiments, a data collection andprocessing system is provided having the use of ambient, local andvibration noise for prediction and having RF identification and aninclinometer. In embodiments, a data collection and processing system isprovided having the use of ambient, local and vibration noise forprediction and having continuous ultrasonic monitoring. In embodiments,a data collection and processing system is provided having the use ofambient, local and vibration noise for prediction and havingcloud-based, machine pattern recognition based on fusion of remote,analog industrial sensors. In embodiments, a data collection andprocessing system is provided having the use of ambient, local andvibration noise for prediction and having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system. Inembodiments, a data collection and processing system is provided havingthe use of ambient, local and vibration noise for prediction and havingcloud-based policy automation engine for IoT, with creation, deployment,and management of IoT devices. In embodiments, a data collection andprocessing system is provided having the use of ambient, local andvibration noise for prediction and having on-device sensor fusion anddata storage for industrial IoT devices. In embodiments, a datacollection and processing system is provided having the use of ambient,local and vibration noise for prediction and having a self-organizingdata marketplace for industrial IoT data. In embodiments, a datacollection and processing system is provided having the use of ambient,local and vibration noise for prediction and having self-organization ofdata pools based on utilization and/or yield metrics. In embodiments, adata collection and processing system is provided having the use ofambient, local and vibration noise for prediction and having training AImodels based on industry-specific feedback. In embodiments, a datacollection and processing system is provided having the use of ambient,local and vibration noise for prediction and having a self-organizedswarm of industrial data collectors. In embodiments, a data collectionand processing system is provided having the use of ambient, local andvibration noise for prediction and having an IoT distributed ledger. Inembodiments, a data collection and processing system is provided havingthe use of ambient, local and vibration noise for prediction and havinga self-organizing collector. In embodiments, a data collection andprocessing system is provided having the use of ambient, local andvibration noise for prediction and having a network-sensitive collector.In embodiments, a data collection and processing system is providedhaving the use of ambient, local and vibration noise for prediction andhaving a remotely organized collector. In embodiments, a data collectionand processing system is provided having the use of ambient, local andvibration noise for prediction and having a self-organizing storage fora multi-sensor data collector. In embodiments, a data collection andprocessing system is provided having the use of ambient, local andvibration noise for prediction and having a self-organizing networkcoding for multi-sensor data network. In embodiments, a data collectionand processing system is provided having the use of ambient, local andvibration noise for prediction and having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs. In embodiments, a data collection andprocessing system is provided having the use of ambient, local andvibration noise for prediction and having heat maps displaying collecteddata for AR/VR. In embodiments, a data collection and processing systemis provided having the use of ambient, local and vibration noise forprediction and having automatically tuned AR/VR visualization of datacollected by a data collector.

In embodiments, a data collection and processing system is providedhaving smart route changes route based on incoming data or alarms toenable simultaneous dynamic data for analysis or correlation. Inembodiments, a data collection and processing system is provided havingsmart route changes route based on incoming data or alarms to enablesimultaneous dynamic data for analysis or correlation and having smartODS and transfer functions. In embodiments, a data collection andprocessing system is provided having smart route changes route based onincoming data or alarms to enable simultaneous dynamic data for analysisor correlation and having a hierarchical multiplexer. In embodiments, adata collection and processing system is provided having smart routechanges route based on incoming data or alarms to enable simultaneousdynamic data for analysis or correlation and having identification ofsensor overload. In embodiments, a data collection and processing systemis provided having smart route changes route based on incoming data oralarms to enable simultaneous dynamic data for analysis or correlationand having RF identification and an inclinometer. In embodiments, a datacollection and processing system is provided having smart route changesroute based on incoming data or alarms to enable simultaneous dynamicdata for analysis or correlation and having continuous ultrasonicmonitoring. In embodiments, a data collection and processing system isprovided having smart route changes route based on incoming data oralarms to enable simultaneous dynamic data for analysis or correlationand having cloud-based, machine pattern recognition based on fusion ofremote, analog industrial sensors. In embodiments, a data collection andprocessing system is provided having smart route changes route based onincoming data or alarms to enable simultaneous dynamic data for analysisor correlation and having cloud-based, machine pattern analysis of stateinformation from multiple analog industrial sensors to provideanticipated state information for an industrial system. In embodiments,a data collection and processing system is provided having smart routechanges route based on incoming data or alarms to enable simultaneousdynamic data for analysis or correlation and having cloud-based policyautomation engine for IoT, with creation, deployment, and management ofIoT devices. In embodiments, a data collection and processing system isprovided having smart route changes route based on incoming data oralarms to enable simultaneous dynamic data for analysis or correlationand having on-device sensor fusion and data storage for industrial IoTdevices. In embodiments, a data collection and processing system isprovided having smart route changes route based on incoming data oralarms to enable simultaneous dynamic data for analysis or correlationand having a self-organizing data marketplace for industrial IoT data.In embodiments, a data collection and processing system is providedhaving smart route changes route based on incoming data or alarms toenable simultaneous dynamic data for analysis or correlation and havingself-organization of data pools based on utilization and/or yieldmetrics. In embodiments, a data collection and processing system isprovided having smart route changes route based on incoming data oralarms to enable simultaneous dynamic data for analysis or correlationand having training AI models based on industry-specific feedback. Inembodiments, a data collection and processing system is provided havingsmart route changes route based on incoming data or alarms to enablesimultaneous dynamic data for analysis or correlation and having aself-organized swarm of industrial data collectors. In embodiments, adata collection and processing system is provided having smart routechanges route based on incoming data or alarms to enable simultaneousdynamic data for analysis or correlation and having an IoT distributedledger. In embodiments, a data collection and processing system isprovided having smart route changes route based on incoming data oralarms to enable simultaneous dynamic data for analysis or correlationand having a self-organizing collector. In embodiments, a datacollection and processing system is provided having smart route changesroute based on incoming data or alarms to enable simultaneous dynamicdata for analysis or correlation and having a network-sensitivecollector. In embodiments, a data collection and processing system isprovided having smart route changes route based on incoming data oralarms to enable simultaneous dynamic data for analysis or correlationand having a remotely organized collector. In embodiments, a datacollection and processing system is provided having smart route changesroute based on incoming data or alarms to enable simultaneous dynamicdata for analysis or correlation and having a self-organizing storagefor a multi-sensor data collector. In embodiments, a data collection andprocessing system is provided having smart route changes route based onincoming data or alarms to enable simultaneous dynamic data for analysisor correlation and having a self-organizing network coding formulti-sensor data network. In embodiments, a data collection andprocessing system is provided having smart route changes route based onincoming data or alarms to enable simultaneous dynamic data for analysisor correlation and having a wearable haptic user interface for anindustrial sensor data collector, with vibration, heat, electricaland/or sound outputs. In embodiments, a data collection and processingsystem is provided having smart route changes route based on incomingdata or alarms to enable simultaneous dynamic data for analysis orcorrelation and having heat maps displaying collected data for AR/VR. Inembodiments, a data collection and processing system is provided havingsmart route changes route based on incoming data or alarms to enablesimultaneous dynamic data for analysis or correlation and havingautomatically tuned AR/VR visualization of data collected by a datacollector.

In embodiments, a data collection and processing system is providedhaving smart ODS and transfer functions. In embodiments, a datacollection and processing system is provided having smart ODS andtransfer functions and having a hierarchical multiplexer. Inembodiments, a data collection and processing system is provided havingsmart ODS and transfer functions and having identification of sensoroverload. In embodiments, a data collection and processing system isprovided having smart ODS and transfer functions and having RFidentification and an inclinometer. In embodiments, a data collectionand processing system is provided having smart ODS and transferfunctions and having continuous ultrasonic monitoring. In embodiments, adata collection and processing system is provided having smart ODS andtransfer functions and having cloud-based, machine pattern recognitionbased on fusion of remote, analog industrial sensors. In embodiments, adata collection and processing system is provided having smart ODS andtransfer functions and having cloud-based, machine pattern analysis ofstate information from multiple analog industrial sensors to provideanticipated state information for an industrial system. In embodiments,a data collection and processing system is provided having smart ODS andtransfer functions and having cloud-based policy automation engine forIoT, with creation, deployment, and management of IoT devices. Inembodiments, a data collection and processing system is provided havingsmart ODS and transfer functions and having on-device sensor fusion anddata storage for industrial IoT devices. In embodiments, a datacollection and processing system is provided having smart ODS andtransfer functions and having a self-organizing data marketplace forindustrial IoT data. In embodiments, a data collection and processingsystem is provided having smart ODS and transfer functions and havingself-organization of data pools based on utilization and/or yieldmetrics. In embodiments, a data collection and processing system isprovided having smart ODS and transfer functions and having training AImodels based on industry-specific feedback. In embodiments, a datacollection and processing system is provided having smart ODS andtransfer functions and having a self-organized swarm of industrial datacollectors. In embodiments, a data collection and processing system isprovided having smart ODS and transfer functions and having an IoTdistributed ledger. In embodiments, a data collection and processingsystem is provided having smart ODS and transfer functions and having aself-organizing collector. In embodiments, a data collection andprocessing system is provided having smart ODS and transfer functionsand having a network-sensitive collector. In embodiments, a datacollection and processing system is provided having smart ODS andtransfer functions and having a remotely organized collector. Inembodiments, a data collection and processing system is provided havingsmart ODS and transfer functions and having a self-organizing storagefor a multi-sensor data collector. In embodiments, a data collection andprocessing system is provided having smart ODS and transfer functionsand having a self-organizing network coding for multi-sensor datanetwork. In embodiments, a data collection and processing system isprovided having smart ODS and transfer functions and having a wearablehaptic user interface for an industrial sensor data collector, withvibration, heat, electrical and/or sound outputs. In embodiments, a datacollection and processing system is provided having smart ODS andtransfer functions and having heat maps displaying collected data forAR/VR. In embodiments, a data collection and processing system isprovided having smart ODS and transfer functions and havingautomatically tuned AR/VR visualization of data collected by a datacollector.

In embodiments, a data collection and processing system is providedhaving a hierarchical multiplexer. In embodiments, a data collection andprocessing system is provided having a hierarchical multiplexer andhaving identification of sensor overload. In embodiments, a datacollection and processing system is provided having a hierarchicalmultiplexer and having RF identification and an inclinometer. Inembodiments, a data collection and processing system is provided havinga hierarchical multiplexer and having continuous ultrasonic monitoring.In embodiments, a data collection and processing system is providedhaving a hierarchical multiplexer and having cloud-based, machinepattern recognition based on fusion of remote, analog industrialsensors. In embodiments, a data collection and processing system isprovided having a hierarchical multiplexer and having cloud-based,machine pattern analysis of state information from multiple analogindustrial sensors to provide anticipated state information for anindustrial system. In embodiments, a data collection and processingsystem is provided having a hierarchical multiplexer and havingcloud-based policy automation engine for IoT, with creation, deployment,and management of IoT devices. In embodiments, a data collection andprocessing system is provided having a hierarchical multiplexer andhaving on-device sensor fusion and data storage for industrial IoTdevices. In embodiments, a data collection and processing system isprovided having a hierarchical multiplexer and having a self-organizingdata marketplace for industrial IoT data. In embodiments, a datacollection and processing system is provided having a hierarchicalmultiplexer and having self-organization of data pools based onutilization and/or yield metrics. In embodiments, a data collection andprocessing system is provided having a hierarchical multiplexer andhaving training AI models based on industry-specific feedback. Inembodiments, a data collection and processing system is provided havinga hierarchical multiplexer and having a self-organized swarm ofindustrial data collectors. In embodiments, a data collection andprocessing system is provided having a hierarchical multiplexer andhaving an IoT distributed ledger. In embodiments, a data collection andprocessing system is provided having a hierarchical multiplexer andhaving a self-organizing collector. In embodiments, a data collectionand processing system is provided having a hierarchical multiplexer andhaving a network-sensitive collector. In embodiments, a data collectionand processing system is provided having a hierarchical multiplexer andhaving a remotely organized collector. In embodiments, a data collectionand processing system is provided having a hierarchical multiplexer andhaving a self-organizing storage for a multi-sensor data collector. Inembodiments, a data collection and processing system is provided havinga hierarchical multiplexer and having a self-organizing network codingfor multi-sensor data network. In embodiments, a data collection andprocessing system is provided having a hierarchical multiplexer andhaving a wearable haptic user interface for an industrial sensor datacollector, with vibration, heat, electrical and/or sound outputs. Inembodiments, a data collection and processing system is provided havinga hierarchical multiplexer and having heat maps displaying collecteddata for AR/VR. In embodiments, a data collection and processing systemis provided having a hierarchical multiplexer and having automaticallytuned AR/VR visualization of data collected by a data collector.

In embodiments, a data collection and processing system is providedhaving identification of sensor overload. In embodiments, a datacollection and processing system is provided having identification ofsensor overload and having RF identification and an inclinometer. Inembodiments, a data collection and processing system is provided havingidentification of sensor overload and having continuous ultrasonicmonitoring. In embodiments, a data collection and processing system isprovided having identification of sensor overload and havingcloud-based, machine pattern recognition based on fusion of remote,analog industrial sensors. In embodiments, a data collection andprocessing system is provided having identification of sensor overloadand having cloud-based, machine pattern analysis of state informationfrom multiple analog industrial sensors to provide anticipated stateinformation for an industrial system. In embodiments, a data collectionand processing system is provided having identification of sensoroverload and having cloud-based policy automation engine for IoT, withcreation, deployment, and management of IoT devices. In embodiments, adata collection and processing system is provided having identificationof sensor overload and having on-device sensor fusion and data storagefor industrial IoT devices. In embodiments, a data collection andprocessing system is provided having identification of sensor overloadand having a self-organizing data marketplace for industrial IoT data.In embodiments, a data collection and processing system is providedhaving identification of sensor overload and having self-organization ofdata pools based on utilization and/or yield metrics. In embodiments, adata collection and processing system is provided having identificationof sensor overload and having training AI models based onindustry-specific feedback. In embodiments, a data collection andprocessing system is provided having identification of sensor overloadand having a self-organized swarm of industrial data collectors. Inembodiments, a data collection and processing system is provided havingidentification of sensor overload and having an IoT distributed ledger.In embodiments, a data collection and processing system is providedhaving identification of sensor overload and having a self-organizingcollector. In embodiments, a data collection and processing system isprovided having identification of sensor overload and having anetwork-sensitive collector. In embodiments, a data collection andprocessing system is provided having identification of sensor overloadand having a remotely organized collector. In embodiments, a datacollection and processing system is provided having identification ofsensor overload and having a self-organizing storage for a multi-sensordata collector. In embodiments, a data collection and processing systemis provided having identification of sensor overload and having aself-organizing network coding for multi-sensor data network. Inembodiments, a data collection and processing system is provided havingidentification of sensor overload and having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical, and/or sound outputs. In embodiments, a data collection andprocessing system is provided having identification of sensor overloadand having heat maps displaying collected data for AR/VR. Inembodiments, a data collection and processing system is provided havingidentification of sensor overload and having automatically tuned AR/VRvisualization of data collected by a data collector.

In embodiments, a data collection and processing system is providedhaving RF identification and an inclinometer. In embodiments, a datacollection and processing system is provided having RF identificationand an inclinometer and having continuous ultrasonic monitoring. Inembodiments, a data collection and processing system is provided havingRF identification and an inclinometer and having cloud-based, machinepattern recognition based on fusion of remote, analog industrialsensors. In embodiments, a data collection and processing system isprovided having RF identification and an inclinometer and havingcloud-based, machine pattern analysis of state information from multipleanalog industrial sensors to provide anticipated state information foran industrial system. In embodiments, a data collection and processingsystem is provided having RF identification and an inclinometer andhaving cloud-based policy automation engine for IoT, with creation,deployment, and management of IoT devices. In embodiments, a datacollection and processing system is provided having RF identificationand an inclinometer and having on-device sensor fusion and data storagefor industrial IoT devices. In embodiments, a data collection andprocessing system is provided having RF identification and aninclinometer and having a self-organizing data marketplace forindustrial IoT data. In embodiments, a data collection and processingsystem is provided having RF identification and an inclinometer andhaving self-organization of data pools based on utilization and/or yieldmetrics. In embodiments, a data collection and processing system isprovided having RF identification and an inclinometer and havingtraining AI models based on industry-specific feedback. In embodiments,a data collection and processing system is provided having RFidentification and an inclinometer and having a self-organized swarm ofindustrial data collectors. In embodiments, a data collection andprocessing system is provided having RF identification and aninclinometer and having an IoT distributed ledger. In embodiments, adata collection and processing system is provided having RFidentification and an inclinometer and having a self-organizingcollector. In embodiments, a data collection and processing system isprovided having RF identification and an inclinometer and having anetwork-sensitive collector. In embodiments, a data collection andprocessing system is provided having RF identification and aninclinometer and having a remotely organized collector. In embodiments,a data collection and processing system is provided having RFidentification and an inclinometer and having a self-organizing storagefor a multi-sensor data collector. In embodiments, a data collection andprocessing system is provided having RF identification and aninclinometer and having a self-organizing network coding formulti-sensor data network. In embodiments, a data collection andprocessing system is provided having RF identification and aninclinometer and having a wearable haptic user interface for anindustrial sensor data collector, with vibration, heat, electrical,and/or sound outputs. In embodiments, a data collection and processingsystem is provided having RF identification and an inclinometer andhaving heat maps displaying collected data for AR/VR. In embodiments, adata collection and processing system is provided having RFidentification and an inclinometer and having automatically tuned AR/VRvisualization of data collected by a data collector.

In embodiments, a data collection and processing system is providedhaving continuous ultrasonic monitoring. In embodiments, a datacollection and processing system is provided having continuousultrasonic monitoring and having cloud-based, machine patternrecognition based on fusion of remote, analog industrial sensors. Inembodiments, a data collection and processing system is provided havingcontinuous ultrasonic monitoring and having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system. Inembodiments, a data collection and processing system is provided havingcontinuous ultrasonic monitoring and having cloud-based policyautomation engine for IoT, with creation, deployment, and management ofIoT devices. In embodiments, a data collection and processing system isprovided having continuous ultrasonic monitoring and having on-devicesensor fusion and data storage for industrial IoT devices. Inembodiments, a data collection and processing system is provided havingcontinuous ultrasonic monitoring and having a self-organizing datamarketplace for industrial IoT data. In embodiments, a data collectionand processing system is provided having continuous ultrasonicmonitoring and having self-organization of data pools based onutilization and/or yield metrics. In embodiments, a data collection andprocessing system is provided having continuous ultrasonic monitoringand having training AI models based on industry-specific feedback. Inembodiments, a data collection and processing system is provided havingcontinuous ultrasonic monitoring and having a self-organized swarm ofindustrial data collectors. In embodiments, a data collection andprocessing system is provided having continuous ultrasonic monitoringand having an IoT distributed ledger. In embodiments, a data collectionand processing system is provided having continuous ultrasonicmonitoring and having a self-organizing collector. In embodiments, adata collection and processing system is provided having continuousultrasonic monitoring and having a network-sensitive collector. Inembodiments, a data collection and processing system is provided havingcontinuous ultrasonic monitoring and having a remotely organizedcollector. In embodiments, a data collection and processing system isprovided having continuous ultrasonic monitoring and having aself-organizing storage for a multi-sensor data collector. Inembodiments, a data collection and processing system is provided havingcontinuous ultrasonic monitoring and having a self-organizing networkcoding for multi-sensor data network. In embodiments, a data collectionand processing system is provided having continuous ultrasonicmonitoring and having a wearable haptic user interface for an industrialsensor data collector, with vibration, heat, electrical, and/or soundoutputs. In embodiments, a data collection and processing system isprovided having continuous ultrasonic monitoring and having heat mapsdisplaying collected data for AR/VR. In embodiments, a data collectionand processing system is provided having continuous ultrasonicmonitoring and having automatically tuned AR/VR visualization of datacollected by a data collector.

In embodiments, a platform is provided having cloud-based, machinepattern recognition based on fusion of remote, analog industrialsensors. In embodiments, a platform is provided having cloud-based,machine pattern recognition based on fusion of remote, analog industrialsensors and having cloud-based, machine pattern analysis of stateinformation from multiple analog industrial sensors to provideanticipated state information for an industrial system. In embodiments,a platform is provided having cloud-based, machine pattern recognitionbased on fusion of remote, analog industrial sensors and havingcloud-based policy automation engine for IoT, with creation, deployment,and management of IoT devices. In embodiments, a platform is providedhaving cloud-based, machine pattern recognition based on fusion ofremote, analog industrial sensors and having on-device sensor fusion anddata storage for industrial IoT devices. In embodiments, a platform isprovided having cloud-based, machine pattern recognition based on fusionof remote, analog industrial sensors and having a self-organizing datamarketplace for industrial IoT data. In embodiments, a platform isprovided having cloud-based, machine pattern recognition based on fusionof remote, analog industrial sensors and having self-organization ofdata pools based on utilization and/or yield metrics. In embodiments, aplatform is provided having cloud-based, machine pattern recognitionbased on fusion of remote, analog industrial sensors and having trainingAI models based on industry-specific feedback. In embodiments, aplatform is provided having cloud-based, machine pattern recognitionbased on fusion of remote, analog industrial sensors and having aself-organized swarm of industrial data collectors. In embodiments, aplatform is provided having cloud-based, machine pattern recognitionbased on fusion of remote, analog industrial sensors and having an IoTdistributed ledger. In embodiments, a platform is provided havingcloud-based, machine pattern recognition based on fusion of remote,analog industrial sensors and having a self-organizing collector. Inembodiments, a platform is provided having cloud-based, machine patternrecognition based on fusion of remote, analog industrial sensors andhaving a network-sensitive collector. In embodiments, a platform isprovided having cloud-based, machine pattern recognition based on fusionof remote, analog industrial sensors and having a remotely organizedcollector. In embodiments, a platform is provided having cloud-based,machine pattern recognition based on fusion of remote, analog industrialsensors and having a self-organizing storage for a multi-sensor datacollector. In embodiments, a platform is provided having cloud-based,machine pattern recognition based on fusion of remote, analog industrialsensors and having a self-organizing network coding for multi-sensordata network. In embodiments, a platform is provided having cloud-based,machine pattern recognition based on fusion of remote, analog industrialsensors and having a wearable haptic user interface for an industrialsensor data collector, with vibration, heat, electrical and/or soundoutputs. In embodiments, a platform is provided having cloud-based,machine pattern recognition based on fusion of remote, analog industrialsensors and having heat maps displaying collected data for AR/VR. Inembodiments, a platform is provided having cloud-based, machine patternrecognition based on fusion of remote, analog industrial sensors andhaving automatically tuned AR/VR visualization of data collected by adata collector.

In embodiments, a platform is provided having cloud-based, machinepattern analysis of state information from multiple analog industrialsensors to provide anticipated state information for an industrialsystem. In embodiments, a platform is provided having cloud-based,machine pattern analysis of state information from multiple analogindustrial sensors to provide anticipated state information for anindustrial system and having cloud-based policy automation engine forIoT, with creation, deployment, and management of IoT devices. Inembodiments, a platform is provided having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system andhaving on-device sensor fusion and data storage for industrial IoTdevices. In embodiments, a platform is provided having cloud-based,machine pattern analysis of state information from multiple analogindustrial sensors to provide anticipated state information for anindustrial system and having a self-organizing data marketplace forindustrial IoT data. In embodiments, a platform is provided havingcloud-based, machine pattern analysis of state information from multipleanalog industrial sensors to provide anticipated state information foran industrial system and having self-organization of data pools based onutilization and/or yield metrics. In embodiments, a platform is providedhaving cloud-based, machine pattern analysis of state information frommultiple analog industrial sensors to provide anticipated stateinformation for an industrial system and having training AI models basedon industry-specific feedback. In embodiments, a platform is providedhaving cloud-based, machine pattern analysis of state information frommultiple analog industrial sensors to provide anticipated stateinformation for an industrial system and having a self-organized swarmof industrial data collectors. In embodiments, a platform is providedhaving cloud-based, machine pattern analysis of state information frommultiple analog industrial sensors to provide anticipated stateinformation for an industrial system and having an IoT distributedledger. In embodiments, a platform is provided having cloud-based,machine pattern analysis of state information from multiple analogindustrial sensors to provide anticipated state information for anindustrial system and having a self-organizing collector. Inembodiments, a platform is provided having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system andhaving a network-sensitive collector. In embodiments, a platform isprovided having cloud-based, machine pattern analysis of stateinformation from multiple analog industrial sensors to provideanticipated state information for an industrial system and having aremotely organized collector. In embodiments, a platform is providedhaving cloud-based, machine pattern analysis of state information frommultiple analog industrial sensors to provide anticipated stateinformation for an industrial system and having a self-organizingstorage for a multi-sensor data collector. In embodiments, a platform isprovided having cloud-based, machine pattern analysis of stateinformation from multiple analog industrial sensors to provideanticipated state information for an industrial system and having aself-organizing network coding for multi-sensor data network. Inembodiments, a platform is provided having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system andhaving a wearable haptic user interface for an industrial sensor datacollector, with vibration, heat, electrical and/or sound outputs. Inembodiments, a platform is provided having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system andhaving heat maps displaying collected data for AR/VR. In embodiments, aplatform is provided having cloud-based, machine pattern analysis ofstate information from multiple analog industrial sensors to provideanticipated state information for an industrial system and havingautomatically tuned AR/VR visualization of data collected by a datacollector.

In embodiments, a platform is provided having cloud-based policyautomation engine for IoT, with creation, deployment, and management ofIoT devices. In embodiments, a platform is provided having cloud-basedpolicy automation engine for IoT, with creation, deployment, andmanagement of IoT devices and having on-device sensor fusion and datastorage for industrial IoT devices. In embodiments, a platform isprovided having cloud-based policy automation engine for IoT, withcreation, deployment, and management of IoT devices and having aself-organizing data marketplace for industrial IoT data. Inembodiments, a platform is provided having cloud-based policy automationengine for IoT, with creation, deployment, and management of IoT devicesand having self-organization of data pools based on utilization and/oryield metrics. In embodiments, a platform is provided having cloud-basedpolicy automation engine for IoT, with creation, deployment, andmanagement of IoT devices and having training AI models based onindustry-specific feedback. In embodiments, a platform is providedhaving cloud-based policy automation engine for IoT, with creation,deployment, and management of IoT devices and having a self-organizedswarm of industrial data collectors. In embodiments, a platform isprovided having cloud-based policy automation engine for IoT, withcreation, deployment, and management of IoT devices and having an IoTdistributed ledger. In embodiments, a platform is provided havingcloud-based policy automation engine for IoT, with creation, deployment,and management of IoT devices and having a self-organizing collector. Inembodiments, a platform is provided having cloud-based policy automationengine for IoT, with creation, deployment, and management of IoT devicesand having a network-sensitive collector. In embodiments, a platform isprovided having cloud-based policy automation engine for IoT, withcreation, deployment, and management of IoT devices and having aremotely organized collector. In embodiments, a platform is providedhaving cloud-based policy automation engine for IoT, with creation,deployment, and management of IoT devices and having a self-organizingstorage for a multi-sensor data collector. In embodiments, a platform isprovided having cloud-based policy automation engine for IoT, withcreation, deployment, and management of IoT devices and having aself-organizing network coding for multi-sensor data network. Inembodiments, a platform is provided having cloud-based policy automationengine for IoT, with creation, deployment, and management of IoT devicesand having a wearable haptic user interface for an industrial sensordata collector, with vibration, heat, electrical and/or sound outputs.In embodiments, a platform is provided having cloud-based policyautomation engine for IoT, with creation, deployment, and management ofIoT devices and having heat maps displaying collected data for AR/VR. Inembodiments, a platform is provided having cloud-based policy automationengine for IoT, with creation, deployment, and management of IoT devicesand having automatically tuned AR/VR visualization of data collected bya data collector.

In embodiments, a platform is provided having on-device sensor fusionand data storage for industrial IoT devices. In embodiments, a platformis provided having on-device sensor fusion and data storage forindustrial IoT devices and having a self-organizing data marketplace forindustrial IoT data. In embodiments, a platform is provided havingon-device sensor fusion and data storage for industrial IoT devices andhaving self-organization of data pools based on utilization and/or yieldmetrics. In embodiments, a platform is provided having on-device sensorfusion and data storage for industrial IoT devices and having trainingAI models based on industry-specific feedback. In embodiments, aplatform is provided having on-device sensor fusion and data storage forindustrial IoT devices and having a self-organized swarm of industrialdata collectors. In embodiments, a platform is provided having on-devicesensor fusion and data storage for industrial IoT devices and having anIoT distributed ledger. In embodiments, a platform is provided havingon-device sensor fusion and data storage for industrial IoT devices andhaving a self-organizing collector. In embodiments, a platform isprovided having on-device sensor fusion and data storage for industrialIoT devices and having a network-sensitive collector. In embodiments, aplatform is provided having on-device sensor fusion and data storage forindustrial IoT devices and having a remotely organized collector. Inembodiments, a platform is provided having on-device sensor fusion anddata storage for industrial IoT devices and having a self-organizingstorage for a multi-sensor data collector. In embodiments, a platform isprovided having on-device sensor fusion and data storage for industrialIoT devices and having a self-organizing network coding for multi-sensordata network. In embodiments, a platform is provided having on-devicesensor fusion and data storage for industrial IoT devices and having awearable haptic user interface for an industrial sensor data collector,with vibration, heat, electrical and/or sound outputs. In embodiments, aplatform is provided having on-device sensor fusion and data storage forindustrial IoT devices and having heat maps displaying collected datafor AR/VR. In embodiments, a platform is provided having on-devicesensor fusion and data storage for industrial IoT devices and havingautomatically tuned AR/VR visualization of data collected by a datacollector.

In embodiments, a platform is provided having a self-organizing datamarketplace for industrial IoT data. In embodiments, a platform isprovided having a self-organizing data marketplace for industrial IoTdata and having self-organization of data pools based on utilizationand/or yield metrics. In embodiments, a platform is provided having aself-organizing data marketplace for industrial IoT data and havingtraining AI models based on industry-specific feedback. In embodiments,a platform is provided having a self-organizing data marketplace forindustrial IoT data and having a self-organized swarm of industrial datacollectors. In embodiments, a platform is provided having aself-organizing data marketplace for industrial IoT data and having anIoT distributed ledger. In embodiments, a platform is provided having aself-organizing data marketplace for industrial IoT data and having aself-organizing collector. In embodiments, a platform is provided havinga self-organizing data marketplace for industrial IoT data and having anetwork-sensitive collector. In embodiments, a platform is providedhaving a self-organizing data marketplace for industrial IoT data andhaving a remotely organized collector. In embodiments, a platform isprovided having a self-organizing data marketplace for industrial IoTdata and having a self-organizing storage for a multi-sensor datacollector. In embodiments, a platform is provided having aself-organizing data marketplace for industrial IoT data and having aself-organizing network coding for multi-sensor data network. Inembodiments, a platform is provided having a self-organizing datamarketplace for industrial IoT data and having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs. In embodiments, a platform is providedhaving a self-organizing data marketplace for industrial IoT data andhaving heat maps displaying collected data for AR/VR. In embodiments, aplatform is provided having a self-organizing data marketplace forindustrial IoT data and having automatically tuned AR/VR visualizationof data collected by a data collector.

In embodiments, platform is provided having self-organization of datapools based on utilization and/or yield metrics. In embodiments,platform is provided having self-organization of data pools based onutilization and/or yield metrics and having training AI models based onindustry-specific feedback. In embodiments, platform is provided havingself-organization of data pools based on utilization and/or yieldmetrics and having a self-organized swarm of industrial data collectors.In embodiments, platform is provided having self-organization of datapools based on utilization and/or yield metrics and having an IoTdistributed ledger. In embodiments, platform is provided havingself-organization of data pools based on utilization and/or yieldmetrics and having a self-organizing collector. In embodiments, platformis provided having self-organization of data pools based on utilizationand/or yield metrics and having a network-sensitive collector. Inembodiments, platform is provided having self-organization of data poolsbased on utilization and/or yield metrics and having a remotelyorganized collector. In embodiments, platform is provided havingself-organization of data pools based on utilization and/or yieldmetrics and having a self-organizing storage for a multi-sensor datacollector. In embodiments, platform is provided having self-organizationof data pools based on utilization and/or yield metrics and having aself-organizing network coding for multi-sensor data network. Inembodiments, platform is provided having self-organization of data poolsbased on utilization and/or yield metrics and having a wearable hapticuser interface for an industrial sensor data collector, with vibration,heat, electrical and/or sound outputs. In embodiments, platform isprovided having self-organization of data pools based on utilizationand/or yield metrics and having heat maps displaying collected data forAR/VR. In embodiments, platform is provided having self-organization ofdata pools based on utilization and/or yield metrics and havingautomatically tuned AR/VR visualization of data collected by a datacollector.

In embodiments, a platform is provided having training AI models basedon industry-specific feedback. In embodiments, a platform is providedhaving training AI models based on industry-specific feedback and havinga self-organized swarm of industrial data collectors. In embodiments, aplatform is provided having training AI models based onindustry-specific feedback and having an IoT distributed ledger. Inembodiments, a platform is provided having training AI models based onindustry-specific feedback and having a self-organizing collector. Inembodiments, a platform is provided having training AI models based onindustry-specific feedback and having a network-sensitive collector. Inembodiments, a platform is provided having training AI models based onindustry-specific feedback and having a remotely organized collector. Inembodiments, a platform is provided having training AI models based onindustry-specific feedback and having a self-organizing storage for amulti-sensor data collector. In embodiments, a platform is providedhaving training AI models based on industry-specific feedback and havinga self-organizing network coding for multi-sensor data network. Inembodiments, a platform is provided having training AI models based onindustry-specific feedback and having a wearable haptic user interfacefor an industrial sensor data collector, with vibration, heat,electrical, and/or sound outputs. In embodiments, a platform is providedhaving training AI models based on industry-specific feedback and havingheat maps displaying collected data for AR/VR. In embodiments, aplatform is provided having training AI models based onindustry-specific feedback and having automatically tuned AR/VRvisualization of data collected by a data collector.

In embodiments, a platform is provided having a self-organized swarm ofindustrial data collectors. In embodiments, a platform is providedhaving a self-organized swarm of industrial data collectors and havingan IoT distributed ledger. In embodiments, a platform is provided havinga self-organized swarm of industrial data collectors and having aself-organizing collector. In embodiments, a platform is provided havinga self-organized swarm of industrial data collectors and having anetwork-sensitive collector. In embodiments, a platform is providedhaving a self-organized swarm of industrial data collectors and having aremotely organized collector. In embodiments, a platform is providedhaving a self-organized swarm of industrial data collectors and having aself-organizing storage for a multi-sensor data collector. Inembodiments, a platform is provided having a self-organized swarm ofindustrial data collectors and having a self-organizing network codingfor multi-sensor data network. In embodiments, a platform is providedhaving a self-organized swarm of industrial data collectors and having awearable haptic user interface for an industrial sensor data collector,with vibration, heat, electrical and/or sound outputs. In embodiments, aplatform is provided having a self-organized swarm of industrial datacollectors and having heat maps displaying collected data for AR/VR. Inembodiments, a platform is provided having a self-organized swarm ofindustrial data collectors and having automatically tuned AR/VRvisualization of data collected by a data collector.

In embodiments, a platform is provided having an IoT distributed ledger.In embodiments, a platform is provided having an IoT distributed ledgerand having a self-organizing collector. In embodiments, a platform isprovided having an IoT distributed ledger and having a network-sensitivecollector. In embodiments, a platform is provided having an IoTdistributed ledger and having a remotely organized collector. Inembodiments, a platform is provided having an IoT distributed ledger andhaving a self-organizing storage for a multi-sensor data collector. Inembodiments, a platform is provided having an IoT distributed ledger andhaving a self-organizing network coding for multi-sensor data network.In embodiments, a platform is provided having an IoT distributed ledgerand having a wearable haptic user interface for an industrial sensordata collector, with vibration, heat, electrical and/or sound outputs.In embodiments, a platform is provided having an IoT distributed ledgerand having heat maps displaying collected data for AR/VR. Inembodiments, a platform is provided having an IoT distributed ledger andhaving automatically tuned AR/VR visualization of data collected by adata collector.

In embodiments, a platform is provided having a self-organizingcollector. In embodiments, a platform is provided having aself-organizing collector and having a network-sensitive collector. Inembodiments, a platform is provided having a self-organizing collectorand having a remotely organized collector. In embodiments, a platform isprovided having a self-organizing collector and having a self-organizingstorage for a multi-sensor data collector. In embodiments, a platform isprovided having a self-organizing collector and having a self-organizingnetwork coding for multi-sensor data network. In embodiments, a platformis provided having a self-organizing collector and having a wearablehaptic user interface for an industrial sensor data collector, withvibration, heat, electrical and/or sound outputs. In embodiments, aplatform is provided having a self-organizing collector and having heatmaps displaying collected data for AR/VR. In embodiments, a platform isprovided having a self-organizing collector and having automaticallytuned AR/VR visualization of data collected by a data collector.

In embodiments, a platform is provided having a network-sensitivecollector. In embodiments, a platform is provided having anetwork-sensitive collector and having a remotely organized collector.In embodiments, a platform is provided having a network-sensitivecollector and having a self-organizing storage for a multi-sensor datacollector. In embodiments, a platform is provided having anetwork-sensitive collector and having a self-organizing network codingfor multi-sensor data network. In embodiments, a platform is providedhaving a network-sensitive collector and having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs. In embodiments, a platform is providedhaving a network-sensitive collector and having heat maps displayingcollected data for AR/VR. In embodiments, a platform is provided havinga network-sensitive collector and having automatically tuned AR/VRvisualization of data collected by a data collector.

In embodiments, a platform is provided having a remotely organizedcollector. In embodiments, a platform is provided having a remotelyorganized collector and having a self-organizing storage for amulti-sensor data collector. In embodiments, a platform is providedhaving a remotely organized collector and having a self-organizingnetwork coding for multi-sensor data network. In embodiments, a platformis provided having a remotely organized collector and having a wearablehaptic user interface for an industrial sensor data collector, withvibration, heat, electrical and/or sound outputs. In embodiments, aplatform is provided having a remotely organized collector and havingheat maps displaying collected data for AR/VR. In embodiments, aplatform is provided having a remotely organized collector and havingautomatically tuned AR/VR visualization of data collected by a datacollector.

In embodiments, a platform is provided having a self-organizing storagefor a multi-sensor data collector. In embodiments, a platform isprovided having a self-organizing storage for a multi-sensor datacollector and having a self-organizing network coding for multi-sensordata network. In embodiments, a platform is provided having aself-organizing storage for a multi-sensor data collector and having awearable haptic user interface for an industrial sensor data collector,with vibration, heat, electrical and/or sound outputs. In embodiments, aplatform is provided having a self-organizing storage for a multi-sensordata collector and having heat maps displaying collected data for AR/VR.In embodiments, a platform is provided having a self-organizing storagefor a multi-sensor data collector and having automatically tuned AR/VRvisualization of data collected by a data collector.

In embodiments, a platform is provided having a self-organizing networkcoding for multi-sensor data network. In embodiments, a platform isprovided having a self-organizing network coding for multi-sensor datanetwork and having a wearable haptic user interface for an industrialsensor data collector, with vibration, heat, electrical, and/or soundoutputs. In embodiments, a platform is provided having a self-organizingnetwork coding for multi-sensor data network and having heat mapsdisplaying collected data for AR/VR. In embodiments, a platform isprovided having a self-organizing network coding for multi-sensor datanetwork and having automatically tuned AR/VR visualization of datacollected by a data collector. In embodiments, a platform is providedhaving a wearable haptic user interface for an industrial sensor datacollector, with vibration, heat, electrical and/or sound outputs. Inembodiments, a platform is provided having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs and having heat maps displayingcollected data for AR/VR. In embodiments, a platform is provided havinga wearable haptic user interface for an industrial sensor datacollector, with vibration, heat, electrical and/or sound outputs andhaving automatically tuned AR/VR visualization of data collected by adata collector. In embodiments, a platform is provided having heat mapsdisplaying collected data for AR/VR. In embodiments, a platform isprovided having heat maps displaying collected data for AR/VR and havingautomatically tuned AR/VR visualization of data collected by a datacollector.

While only a few embodiments of the present disclosure have been shownand described, it will be obvious to those skilled in the art that manychanges and modifications may be made thereunto without departing fromthe spirit and scope of the present disclosure as described in thefollowing claims. All patent applications and patents, both foreign anddomestic, and all other publications referenced herein are incorporatedherein in their entireties to the full extent permitted by law.

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. The present disclosure may beimplemented as a method on the machine, as a system or apparatus as partof or in relation to the machine, or as a computer program productembodied in a computer readable medium executing on one or more of themachines. In embodiments, the processor may be part of a server, cloudserver, client, network infrastructure, mobile computing platform,stationary computing platform, or other computing platform. A processormay be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions, and thelike. The processor may be or may include a signal processor, digitalprocessor, embedded processor, microprocessor, or any variant such as aco-processor (math co-processor, graphic co-processor, communicationco-processor, and the like) and the like that may directly or indirectlyfacilitate execution of program code or program instructions storedthereon. In addition, the processor may enable execution of multipleprograms, threads, and codes. The threads may be executed simultaneouslyto enhance the performance of the processor and to facilitatesimultaneous operations of the application. By way of implementation,methods, program codes, program instructions and the like describedherein may be implemented in one or more thread. The thread may spawnother threads that may have assigned priorities associated with them;the processor may execute these threads based on priority or any otherorder based on instructions provided in the program code. The processor,or any machine utilizing one, may include non-transitory memory thatstores methods, codes, instructions, and programs as described hereinand elsewhere. The processor may access a non-transitory storage mediumthrough an interface that may store methods, codes, and instructions asdescribed herein and elsewhere. The storage medium associated with theprocessor for storing methods, programs, codes, program instructions orother type of instructions capable of being executed by the computing orprocessing device may include but may not be limited to one or more of aCD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache, and thelike.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,internet server, intranet server, cloud server, and other variants suchas secondary server, host server, distributed server, and the like. Theserver may include one or more of memories, processors, computerreadable transitory and/or non-transitory media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other servers, clients, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs, or codes asdescribed herein and elsewhere may be executed by the server. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers,social networks, and the like. Additionally, this coupling and/orconnection may facilitate remote execution of program across thenetwork. The networking of some or all of these devices may facilitateparallel processing of a program or method at one or more locationwithout deviating from the scope of the disclosure. In addition, any ofthe devices attached to the server through an interface may include atleast one storage medium capable of storing methods, programs, codeand/or instructions. A central repository may provide programinstructions to be executed on different devices. In thisimplementation, the remote repository may act as a storage medium forprogram code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, internet client, intranetclient and other variants such as secondary client, host client,distributed client, and the like. The client may include one or more ofmemories, processors, computer readable transitory and/or non-transitorymedia, storage media, ports (physical and virtual), communicationdevices, and interfaces capable of accessing other clients, servers,machines, and devices through a wired or a wireless medium, and thelike. The methods, programs, or codes as described herein and elsewheremay be executed by the client. In addition, other devices required forexecution of methods as described in this application may be consideredas a part of the infrastructure associated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers, andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe disclosure. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM, and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements. The methods and systems describedherein may be configured for use with any kind of private, community, orhybrid cloud computing network or cloud computing environment, includingthose which involve features of software as a service (“SaaS”), platformas a service (“PaaS”), and/or infrastructure as a service (“IaaS”).

The methods, program codes, and instructions described herein andelsewhere may be implemented on a cellular network having multiplecells. The cellular network may either be frequency division multipleaccess (“FDMA”) network or code division multiple access (“CDMA”)network. The cellular network may include mobile devices, cell sites,base stations, repeaters, antennas, towers, and the like. The cellnetwork may be a GSM, GPRS, 3G, EVDO, mesh, or other networks types.

The methods, program codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic books readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on apeer-to-peer network, mesh network, or other communications network. Theprogram code may be stored on the storage medium associated with theserver and executed by a computing device embedded within the server.The base station may include a computing device and a storage medium.The storage device may store program codes and instructions executed bythe computing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable transitory and/or non-transitorymedia that may include: computer components, devices, and recordingmedia that retain digital data used for computing for some interval oftime; semiconductor storage known as random access memory (“RAM”); massstorage typically for more permanent storage, such as optical discs,forms of magnetic storage like hard disks, tapes, drums, cards and othertypes; processor registers, cache memory, volatile memory, non-volatilememory; optical storage such as CD, DVD; removable media such as flashmemory (e.g., USB sticks or keys), floppy disks, magnetic tape, papertape, punch cards, standalone RAM disks, zip drives, removable massstorage, off-line, and the like; other computer memory such as dynamicmemory, static memory, read/write storage, mutable storage, read only,random access, sequential access, location addressable, fileaddressable, content addressable, network attached storage, storage areanetwork, bar codes, magnetic ink, and the like.

The methods and systems described herein may transform physical and/oror intangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flow charts andblock diagrams throughout the Figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable transitory and/ornon-transitory media having a processor capable of executing programinstructions stored thereon as a monolithic software structure, asstandalone software modules, or as modules that employ externalroutines, code, services, and so forth, or any combination of these, andall such implementations may be within the scope of the presentdisclosure. Examples of such machines may include, but may not belimited to, personal digital assistants, laptops, personal computers,mobile phones, other handheld computing devices, medical equipment,wired or wireless communication devices, transducers, chips,calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipment, servers, routers, and the like.Furthermore, the elements depicted in the flow chart and block diagramsor any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the foregoing drawingsand descriptions set forth functional aspects of the disclosed systems,no particular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it will beappreciated that the various steps identified and described above may bevaried, and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps associatedtherewith, may be realized in hardware, software or any combination ofhardware and software suitable for a particular application. Thehardware may include a general-purpose computer and/or dedicatedcomputing device or specific computing device or particular aspect orcomponent of a specific computing device. The processes may be realizedin one or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable device, along with internal and/or external memory. Theprocesses may also, or instead, be embodied in an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. It will further be appreciated that one ormore of the processes may be realized as a computer executable codecapable of being executed on a machine-readable medium.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions.

Thus, in one aspect, methods described above and combinations thereofmay be embodied in computer executable code that, when executing on oneor more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

While the disclosure has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present disclosure isnot to be limited by the foregoing examples, but is to be understood inthe broadest sense allowable by law.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosure (especially in the context of thefollowing claims) is to be construed to cover both the singular and theplural, unless otherwise indicated herein or clearly contradicted bycontext. The terms “comprising,” “having,” “including,” and “containing”are to be construed as open-ended terms (i.e., meaning “including, butnot limited to,”) unless otherwise noted. Recitation of ranges of valuesherein are merely intended to serve as a shorthand method of referringindividually to each separate value falling within the range, unlessotherwise indicated herein, and each separate value is incorporated intothe specification as if it were individually recited herein. All methodsdescribed herein can be performed in any suitable order unless otherwiseindicated herein or otherwise clearly contradicted by context. The useof any and all examples, or exemplary language (e.g., “such as”)provided herein, is intended merely to better illuminate the disclosure,and does not pose a limitation on the scope of the disclosure unlessotherwise claimed. No language in the specification should be construedas indicating any non-claimed element as essential to the practice ofthe disclosure.

While the foregoing written description enables one skilled in the artto make and use what is considered presently to be the best modethereof, those skilled in the art will understand and appreciate theexistence of variations, combinations, and equivalents of the specificembodiment, method, and examples herein. The disclosure should thereforenot be limited by the above described embodiment, method, and examples,but by all embodiments and methods within the scope and spirit of thedisclosure.

Any element in a claim that does not explicitly state “means for”performing a specified function, or “step for” performing a specifiedfunction, is not to be interpreted as a “means” or “step” clause asspecified in 35 U.S.C. § 112(f). In particular, any use of “step of” inthe claims is not intended to invoke the provision of 35 U.S.C. §112(f).

Persons skilled in the art may appreciate that numerous designconfigurations may be possible to enjoy the functional benefits of theinventive systems. Thus, given the wide variety of configurations andarrangements of embodiments of the present invention, the scope of theinvention is reflected by the breadth of the claims below rather thannarrowed by the embodiments described above.

What is claimed is:
 1. A method for data collection, processing, andutilization of signals with a platform monitoring at least a firstelement in a first machine in an industrial environment, the methodcomprising: obtaining, automatically with a computing environment, atleast a first sensor signal and a second sensor signal with a local datacollection system that monitors at least the first machine; connecting afirst input of a crosspoint switch of the local data collection systemto a first sensor and a second input of the crosspoint switch to asecond sensor in the local data collection system; switching between acondition in which a first output of the crosspoint switch alternatesbetween delivery of at least the first sensor signal and the secondsensor signal and a condition in which there is simultaneous delivery ofthe first sensor signal from the first output and the second sensorsignal from a second output of the crosspoint switch; switching offunassigned outputs of the crosspoint switch into a high-impedance state,wherein the local data collection system includes multiple dataacquisition units each having an onboard card set that store calibrationinformation and maintenance history of a data acquisition unit in whichthe onboard card set is located; and continuously monitoring at least athird input of the crosspoint switch with an alarm having apre-determined trigger condition when the third input is unassigned toany of multiple outputs on the crosspoint switch.
 2. The method of claim1 wherein the first sensor signal and the second sensor signal arecontinuous vibration data from the industrial environment.
 3. The methodof claim 1 wherein the second sensor in the local data collection systemis connected to the first machine.
 4. The method of claim 1 wherein thesecond sensor in the local data collection system is connected to asecond machine in the industrial environment.
 5. The method of claim 1further comprising comparing, automatically with the computingenvironment, relative phases of the first and second sensor signals. 6.The method of claim 1 wherein the first sensor is a single-axis sensorand the second sensor is a three-axis sensor.
 7. The method of claim 1wherein at least the first input of the crosspoint switch includesinternet protocol front-end signal conditioning for improvedsignal-to-noise ratio.
 8. The method of claim 1 wherein the local datacollection system includes distributed complex programmable hardwaredevice (CPLD) chips each dedicated to a data bus for logic control ofreceiving the multiple data streams from the multiple machines in theindustrial environment.
 9. The method of claim 8 further comprisingpowering down at least one of an analog sensor channel and a componentboard of the local data collection system.
 10. The method of claim 8wherein each of the distributed CPLD chips includes a high-frequencycrystal clock reference divided by at least one of the distributed CPLDchips for at least one delta-sigma analog-to-digital converter toachieve lower sampling rates without digital resampling.
 11. The methodof claim 1 wherein the local data collection system provideshigh-amperage input capability using solid state relays.
 12. The methodof claim 1 wherein the local data collection system includes an externalvoltage reference for an A/D zero reference that is independent of avoltage of the first sensor and the second sensor.
 13. The method ofclaim 1 wherein the local data collection system includes a phase-lockloop band-pass tracking filter that obtain slow-speed RPMs and phaseinformation.
 14. The method of claim 1 further comprising digitallyderiving phase using on-board timers relative to at least one triggerchannel and at least one of multiple inputs on the crosspoint switch.15. The method of claim 1 further comprising auto-scaling with apeak-detector using a separate analog-to-digital converter for peakdetection.
 16. The method of claim 1 further comprising routing at leastone trigger channel that is one of raw and buffered into at least one ofmultiple inputs on the crosspoint switch.
 17. The method of claim 1further comprising increasing input oversampling rates with at least onedelta-sigma analog-to-digital converter to reduce sampling rate outputsand to minimize anti-aliasing filter requirements.
 18. The method ofclaim 1 further comprising obtaining blocks of data at a single samplingrate with the local data collection system as opposed to multiple setsof data taken at different sampling rates.
 19. The method of claim 18wherein the single sampling rate corresponds to a maximum frequency offorty kilohertz.
 20. The method of claim 18 wherein the long blocks ofdata are for a duration that is in excess of one minute.
 21. The methodof claim 1 wherein the local data collection system receives multipledata streams from multiple machines in the industrial environment. 22.The method of claim 1 further comprising planning data acquisitionroutes based on hierarchical templates associated with at least thefirst element in the first machine in the industrial environment. 23.The method of claim 1 wherein the local data collection system managesdata collection bands that define a specific frequency band and at leastone of a group of spectral peaks, a true-peak level, a crest factorderived from a time waveform, and an overall waveform derived from avibration envelope.
 24. The method of claim 23 wherein the local datacollection system includes a neural net expert system using intelligentmanagement of the data collection bands.
 25. The method of claim 23wherein the local data collection system creates data acquisition routesbased on hierarchical templates that each include the data collectionbands related to machines associated with the data acquisition routes.26. The method of claim 25 wherein at least one of the hierarchicaltemplates is associated with multiple interconnected elements of thefirst machine.
 27. The method of claim 26 wherein at least one of thehierarchical templates is associated with elements of a particular typeassociated with the first machine and with elements of the particulartype associated with a second machine.
 28. The method of claim 25wherein at least one of the hierarchical templates is associated with atleast the first machine being proximate in location to a second machine.29. The method of claim 23 further comprising controlling a graphicaluser interface system of the local data collection system to manage thedata collection bands, wherein the graphical user interface systemincludes an expert system diagnostic tool.
 30. The method of claim 29wherein the computing environment of the platform includes cloud-based,machine pattern analysis of state information from multiple sensors toprovide anticipated state information for the industrial environment.31. The method of claim 29 wherein the computing environment of theplatform provides self-organization of data pools based on at least oneof utilization metrics and yield metrics.
 32. The method of claim 29wherein the computing environment of the platform includes aself-organized swarm of industrial data collectors.
 33. The method ofclaim 29 wherein each of multiple inputs of the crosspoint switch isindividually assignable to any of multiple outputs of the crosspointswitch.
 34. The method of claim 1, wherein the computing environment ofthe platform includes cloud-based, machine pattern analysis of stateinformation from multiple sensors to provide anticipated stateinformation for the industrial environment.